
@inproceedings{vaswani_attention_2017,
	title = {Attention is {All} you {Need}},
	volume = {30},
	url = {https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html},
	abstract = {The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms.  We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.},
	urldate = {2023-11-20},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Łukasz and Polosukhin, Illia},
	year = {2017},
}

@inproceedings{kosiorek_hierarchical_2017,
	title = {Hierarchical {Attentive} {Recurrent} {Tracking}},
	volume = {30},
	url = {https://proceedings.neurips.cc/paper/2017/hash/752d25a1f8dbfb2d656bac3094bfb81c-Abstract.html},
	abstract = {Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate where'' andwhat'' processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos. The first layer of attention discards the majority of background by selecting a region containing the object of interest, while the subsequent layers tune in on visual features particular to the tracked object.    This framework is fully differentiable and can be trained in a purely data driven fashion by gradient methods. To improve training convergence, we augment the loss function with terms for auxiliary tasks relevant for tracking. Evaluation of the proposed model is performed on two datasets: pedestrian tracking on the KTH activity recognition dataset and the more difficult KITTI object tracking dataset.},
	urldate = {2023-11-20},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Kosiorek, Adam and Bewley, Alex and Posner, Ingmar},
	year = {2017},
}

@inproceedings{kahou_ratm_2017,
	title = {{RATM}: {Recurrent} {Attentive} {Tracking} {Model}},
	shorttitle = {{RATM}},
	url = {https://ieeexplore.ieee.org/abstract/document/8014940?casa_token=1kieLiuy2lwAAAAA:aIm9OeXw6zwwaB3mBf1uytcHP8LLwqn_v1R4iHSlvNVs8zy298-tn-TgSI97OSELBq1V84bBMTQ},
	doi = {10.1109/CVPRW.2017.206},
	abstract = {We present an attention-based modular neural framework for computer vision. The framework uses a soft attention mechanism allowing models to be trained with gradient descent. It consists of three modules: a recurrent attention module controlling where to look in an image or video frame, a feature-extraction module providing a representation of what is seen, and an objective module formalizing why the model learns its attentive behavior. The attention module allows the model to focus computation on task-related information in the input. We apply the framework to several object tracking tasks and explore various design choices. We experiment with three data sets, bouncing ball, moving digits and the real-world KTH data set. The proposed RATM performs well on all three tasks and can generalize to related but previously unseen sequences from a challenging tracking data set.},
	urldate = {2023-11-20},
	booktitle = {2017 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} {Workshops} ({CVPRW})},
	author = {Kahou, Samira Ebrahimi and Michalski, Vincent and Memisevic, Roland and Pal, Christopher and Vincent, Pascal},
	month = jul,
	year = {2017},
	note = {ISSN: 2160-7516},
	pages = {1613--1622},
}

@inproceedings{lu_see_2019,
	title = {See {More}, {Know} {More}: {Unsupervised} {Video} {Object} {Segmentation} {With} {Co}-{Attention} {Siamese} {Networks}},
	shorttitle = {See {More}, {Know} {More}},
	url = {https://openaccess.thecvf.com/content_CVPR_2019/html/Lu_See_More_Know_More_Unsupervised_Video_Object_Segmentation_With_Co-Attention_CVPR_2019_paper.html},
	urldate = {2023-11-20},
	author = {Lu, Xiankai and Wang, Wenguan and Ma, Chao and Shen, Jianbing and Shao, Ling and Porikli, Fatih},
	year = {2019},
	pages = {3623--3632},
}

@article{gegenfurtner_interaction_2016,
	title = {The {Interaction} {Between} {Vision} and {Eye} {Movements}},
	volume = {45},
	issn = {0301-0066, 1468-4233},
	url = {http://journals.sagepub.com/doi/10.1177/0301006616657097},
	doi = {10.1177/0301006616657097},
	abstract = {The existence of a central fovea, the small retinal region with high analytical performance, is arguably the most prominent design feature of the primate visual system. This centralization comes along with the corresponding capability to move the eyes to reposition the fovea continuously. Past research on visual perception was mainly concerned with foveal vision while the observers kept their eyes stationary. Research on the role of eye movements in visual perception emphasized their negative aspects, for example, the active suppression of vision before and during the execution of saccades. But is the only benefit of our precise eye movement system to provide high acuity of the small foveal region, at the cost of retinal blur during their execution? In this review, I will compare human visual perception with and without saccadic and smooth pursuit eye movements to emphasize different aspects and functions of eye movements. I will show that the interaction between eye movements and visual perception is optimized for the active sampling of information across the visual field and for the calibration of different parts of the visual field. The movements of our eyes and visual information uptake are intricately intertwined. The two processes interact to enable an optimal perception of the world, one that we cannot fully grasp by doing experiments where observers are fixating a small spot on a display.},
	language = {en},
	number = {12},
	urldate = {2023-11-20},
	journal = {Perception},
	author = {Gegenfurtner, Karl R.},
	month = dec,
	year = {2016},
	pages = {1333--1357},
}

@article{thanikasalam_target-specific_2020,
	title = {Target-{Specific} {Siamese} {Attention} {Network} for {Real}-{Time} {Object} {Tracking}},
	volume = {15},
	issn = {1556-6021},
	url = {https://ieeexplore.ieee.org/abstract/document/8804242?casa_token=lFUlezmsqREAAAAA:DeXJO_EMltBjF3V7pql4acOCCL-gZLfWHt6k25RfLCG2CQxm7fAjzWSfMkXcXm4lwGME8EGGW-Q},
	doi = {10.1109/TIFS.2019.2935871},
	abstract = {Deep similarity trackers are able to track above real-time speed. However, their accuracy is considerably lower than deep classification based trackers since they avoid valuable online cues. To feed the target-specific information for real-time object tracking, we propose a novel Siamese attention network. Different types of attention mechanisms are used to capture different contexts of target information and then learned knowledge is used to feed target cues at different representation levels of similarity tracking. In addition, an online learning mechanism is employed to utilise the available target-specific data. The proposed tracker reduces the impact of noise in the target template and improves the accuracy of similarity tracking by feeding target cues into the similarity search. Extensive evaluation performed on OTB-2013/50/100 and VOT2018 benchmark datasets demonstrate the proposed tracker outperforms state-of-the-art approaches while maintaining real-time tracking speed.},
	urldate = {2023-11-20},
	journal = {IEEE Transactions on Information Forensics and Security},
	author = {Thanikasalam, Kokul and Fookes, Clinton and Sridharan, Sridha and Ramanan, Amirthalingam and Pinidiyaarachchi, Amalka},
	year = {2020},
	note = {Conference Name: IEEE Transactions on Information Forensics and Security},
	pages = {1276--1289},
}

@article{huang_siamatl_2022,
	title = {{SiamATL}: {Online} {Update} of {Siamese} {Tracking} {Network} via {Attentional} {Transfer} {Learning}},
	volume = {52},
	issn = {2168-2275},
	shorttitle = {{SiamATL}},
	url = {https://ieeexplore.ieee.org/abstract/document/9318529?casa_token=WPyz_AqomCcAAAAA:tNwExF6wf4PgSK_mw4oFYUXgBRGI8FNFgWZ3ijofnzTA2pUlhYg7yWD_6qoTeyG1zBl4Zt9AhBI},
	doi = {10.1109/TCYB.2020.3043520},
	abstract = {Visual object tracking with semantic deep features has recently attracted much attention in computer vision. Especially, Siamese trackers, which aim to learn a decision making-based similarity evaluation, are widely utilized in the tracking community. However, the online updating of the Siamese fashion is still a tricky issue due to the limitation, which is a tradeoff between model adaption and degradation. To address such an issue, in this article, we propose a novel attentional transfer learning-based Siamese network (SiamATL), which fully exploits the previous knowledge to inspire the current tracker learning in the decision-making module. First, we explicitly model the template and surroundings by using an attentional online update strategy to avoid template pollution. Then, we introduce an instance-transfer discriminative correlation filter (ITDCF) to enhance the distinguishing ability of the tracker. Finally, we suggest a mutual compensation mechanism that integrates cross-correlation matching and ITDCF detection into the decision-making subnetwork to achieve online tracking. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art tracking algorithms on multiple large-scale tracking datasets.},
	number = {8},
	urldate = {2023-11-20},
	journal = {IEEE Transactions on Cybernetics},
	author = {Huang, Bo and Xu, Tingfa and Shen, Ziyi and Jiang, Shenwang and Zhao, Bingqing and Bian, Ziyang},
	month = aug,
	year = {2022},
	note = {Conference Name: IEEE Transactions on Cybernetics},
	pages = {7527--7540},
}

@misc{sorokin_deep_2015,
	title = {Deep {Attention} {Recurrent} {Q}-{Network}},
	url = {http://arxiv.org/abs/1512.01693},
	doi = {10.48550/arXiv.1512.01693},
	abstract = {A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind's team called the approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and "hard" attention mechanisms. Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN. Moreover, built-in attention mechanisms allow a direct online monitoring of the training process by highlighting the regions of the game screen the agent is focusing on when making decisions.},
	urldate = {2023-11-20},
	publisher = {arXiv},
	author = {Sorokin, Ivan and Seleznev, Alexey and Pavlov, Mikhail and Fedorov, Aleksandr and Ignateva, Anastasiia},
	month = dec,
	year = {2015},
	note = {arXiv:1512.01693 [cs]},
	keywords = {Computer Science - Machine Learning},
}

@article{fiaz_improving_2020,
	title = {Improving {Object} {Tracking} by {Added} {Noise} and {Channel} {Attention}},
	volume = {20},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1424-8220},
	url = {https://www.mdpi.com/1424-8220/20/13/3780},
	doi = {10.3390/s20133780},
	abstract = {CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers.},
	language = {en},
	number = {13},
	urldate = {2023-11-20},
	journal = {Sensors},
	author = {Fiaz, Mustansar and Mahmood, Arif and Baek, Ki Yeol and Farooq, Sehar Shahzad and Jung, Soon Ki},
	month = jan,
	year = {2020},
	note = {Number: 13
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {Siamese networks, attentional mechanism, convolutional neural network, noise regularization, visual tracking},
	pages = {3780},
}

@misc{noauthor_sensors_nodate,
	title = {Sensors {\textbar} {Free} {Full}-{Text} {\textbar} {Improving} {Object} {Tracking} by {Added} {Noise} and {Channel} {Attention}},
	url = {https://www.mdpi.com/1424-8220/20/13/3780},
	urldate = {2023-11-20},
}

@article{denil_learning_2012,
	title = {Learning {Where} to {Attend} with {Deep} {Architectures} for {Image} {Tracking}},
	volume = {24},
	issn = {0899-7667},
	url = {https://doi.org/10.1162/NECO_a_00312},
	doi = {10.1162/NECO_a_00312},
	abstract = {We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways, identity and control, intended to mirror the what and where pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-restricted Boltzmann machines. At each point in time, the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale, and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies that operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a gaussian process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain.},
	number = {8},
	urldate = {2023-11-20},
	journal = {Neural Computation},
	author = {Denil, Misha and Bazzani, Loris and Larochelle, Hugo and de Freitas, Nando},
	month = aug,
	year = {2012},
	pages = {2151--2184},
}

@article{zeng_view_2020,
	title = {View planning in robot active vision: {A} survey of systems, algorithms, and applications},
	volume = {6},
	issn = {2096-0433, 2096-0662},
	shorttitle = {View planning in robot active vision},
	url = {https://link.springer.com/10.1007/s41095-020-0179-3},
	doi = {10.1007/s41095-020-0179-3},
	abstract = {Rapid development of artiﬁcial intelligence motivates researchers to expand the capabilities of intelligent and autonomous robots. In many robotic applications, robots are required to make planning decisions based on perceptual information to achieve diverse goals in an eﬃcient and eﬀective way. The planning problem has been investigated in active robot vision, in which a robot analyzes its environment and its own state in order to move sensors to obtain more useful information under certain constraints. View planning, which aims to ﬁnd the best view sequence for a sensor, is one of the most challenging issues in active robot vision. The quality and eﬃciency of view planning are critical for many robot systems and are inﬂuenced by the nature of their tasks, hardware conditions, scanning states, and planning strategies. In this paper, we ﬁrst summarize some basic concepts of active robot vision, and then review representative work on systems, algorithms and applications from four perspectives: object reconstruction, scene reconstruction, object recognition, and pose estimation. Finally, some potential directions are outlined for future work.},
	language = {en},
	number = {3},
	urldate = {2023-11-20},
	journal = {Computational Visual Media},
	author = {Zeng, Rui and Wen, Yuhui and Zhao, Wang and Liu, Yong-Jin},
	month = sep,
	year = {2020},
	pages = {225--245},
}

@article{liu_effectiveness_2021,
	title = {The effectiveness of eye tracking in the diagnosis of cognitive disorders: {A} systematic review and meta-analysis},
	volume = {16},
	issn = {1932-6203},
	shorttitle = {The effectiveness of eye tracking in the diagnosis of cognitive disorders},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254059},
	doi = {10.1371/journal.pone.0254059},
	abstract = {Background Eye tracking (ET) is a viable marker for the recognition of cognitive disorders. We assessed the accuracy and clinical value of ET for the diagnosis of cognitive disorders in patients. Methods We searched the Medline, Embase, Web of Science, Cochrane Library, and Pubmed databases from inception to March 2, 2021, as well as the reference lists of identified primary studies. We included articles written in English that investigated ET for cognitive disorder patients—Mild cognitive impairment (MCI), Alzheimer’s disease (AD), Amyotrophic lateral sclerosis (ALS), and dementia. Two independent researchers extracted the data and the characteristics of each study; We calculated pooled sensitivities and specificities. A hierarchical summary of receiver performance characteristics (HSROC) model was used to test the diagnostic accuracy of ET for cognitive impairment (CI). Findings 11 studies met the inclusion criteria and were included in qualitative comprehensive analysis. Meta-analysis was performed on 9 trials using Neuropsychological Cognitive Testing (NCT) as the reference standard. The comprehensive sensitivity and specificity of ET for detecting cognitive disorders were 0.75 (95\% CI 0.72–0.79) and 0.73 (95\% CI 0.70 to 0.76), respectively. The combined positive likelihood ratio (LR+) was 2.74 (95\%CI 2.32–3.24) and the negative likelihood ratio (LR−) was 0.27 (95\%CI 0.18–0.42). Conclusions This review showed that ET technology could be used to detect the decline in CI, clinical use of ET techniques in combination with other tools to assess CI can be encouraged.},
	language = {en},
	number = {7},
	urldate = {2023-11-20},
	journal = {PLOS ONE},
	author = {Liu, Zicai and Yang, Zhen and Gu, Yueming and Liu, Huiyu and Wang, Pu},
	month = jul,
	year = {2021},
	note = {Publisher: Public Library of Science},
	keywords = {Alzheimer's disease, Amyotrophic lateral sclerosis, Cognitive impairment, Database searching, Dementia, Eye movements, Metaanalysis, Neuropsychological testing},
	pages = {e0254059},
}

@article{ward_fast_2002,
	title = {Fast hands-free writing by gaze direction},
	volume = {418},
	copyright = {2002 Springer Nature Limited},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/418838a},
	doi = {10.1038/418838a},
	abstract = {Here we describe a method for text entry based on inverse arithmetic coding that relies on gaze direction alone and which is faster and more accurate than using an on-screen keyboard. These benefits are derived from two innovations: the writing task is matched to the capabilities of the eye, and a language model is used to make predictable words and phrases easier to write.},
	language = {en},
	number = {6900},
	urldate = {2023-11-20},
	journal = {Nature},
	author = {Ward, David J. and MacKay, David J. C.},
	month = aug,
	year = {2002},
	note = {Number: 6900
Publisher: Nature Publishing Group},
	keywords = {Humanities and Social Sciences, Science, multidisciplinary},
	pages = {838--838},
}

@inproceedings{bylinskii_learning_2017,
	address = {New York, NY, USA},
	series = {{UIST} '17},
	title = {Learning {Visual} {Importance} for {Graphic} {Designs} and {Data} {Visualizations}},
	isbn = {978-1-4503-4981-9},
	url = {https://dl.acm.org/doi/10.1145/3126594.3126653},
	doi = {10.1145/3126594.3126653},
	abstract = {Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most important content of a visual design can be used for effective summarization or to facilitate retrieval from a database. We present automated models that predict the relative importance of different elements in data visualizations and graphic designs. Our models are neural networks trained on human clicks and importance annotations on hundreds of designs. We collected a new dataset of crowdsourced importance, and analyzed the predictions of our models with respect to ground truth importance and human eye movements. We demonstrate how such predictions of importance can be used for automatic design retargeting and thumbnailing. User studies with hundreds of MTurk participants validate that, with limited post-processing, our importance-driven applications are on par with, or outperform, current state-of-the-art methods, including natural image saliency. We also provide a demonstration of how our importance predictions can be built into interactive design tools to offer immediate feedback during the design process.},
	urldate = {2023-11-20},
	booktitle = {Proceedings of the 30th {Annual} {ACM} {Symposium} on {User} {Interface} {Software} and {Technology}},
	publisher = {Association for Computing Machinery},
	author = {Bylinskii, Zoya and Kim, Nam Wook and O'Donovan, Peter and Alsheikh, Sami and Madan, Spandan and Pfister, Hanspeter and Durand, Fredo and Russell, Bryan and Hertzmann, Aaron},
	month = oct,
	year = {2017},
	keywords = {computer vision, deep learning, eye tracking, graphic design, machine learning, retargeting, saliency, visualization},
	pages = {57--69},
}

@article{padmanaban_optimizing_2017,
	title = {Optimizing virtual reality for all users through gaze-contingent and adaptive focus displays},
	volume = {114},
	url = {https://www.pnas.org/doi/abs/10.1073/pnas.1617251114},
	doi = {10.1073/pnas.1617251114},
	abstract = {From the desktop to the laptop to the mobile device, personal computing platforms evolve over time. Moving forward, wearable computing is widely expected to be integral to consumer electronics and beyond. The primary interface between a wearable computer and a user is often a near-eye display. However, current generation near-eye displays suffer from multiple limitations: they are unable to provide fully natural visual cues and comfortable viewing experiences for all users. At their core, many of the issues with near-eye displays are caused by limitations in conventional optics. Current displays cannot reproduce the changes in focus that accompany natural vision, and they cannot support users with uncorrected refractive errors. With two prototype near-eye displays, we show how these issues can be overcome using display modes that adapt to the user via computational optics. By using focus-tunable lenses, mechanically actuated displays, and mobile gaze-tracking technology, these displays can be tailored to correct common refractive errors and provide natural focus cues by dynamically updating the system based on where a user looks in a virtual scene. Indeed, the opportunities afforded by recent advances in computational optics open up the possibility of creating a computing platform in which some users may experience better quality vision in the virtual world than in the real one.},
	number = {9},
	urldate = {2023-11-14},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Padmanaban, Nitish and Konrad, Robert and Stramer, Tal and Cooper, Emily A. and Wetzstein, Gordon},
	month = feb,
	year = {2017},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	pages = {2183--2188},
}

@inproceedings{feichtenhofer_detect_2017,
	title = {Detect to {Track} and {Track} to {Detect}},
	url = {https://openaccess.thecvf.com/content_iccv_2017/html/Feichtenhofer_Detect_to_Track_ICCV_2017_paper.html},
	urldate = {2023-11-20},
	author = {Feichtenhofer, Christoph and Pinz, Axel and Zisserman, Andrew},
	year = {2017},
	pages = {3038--3046},
}

@article{luo_multiple_2021,
	title = {Multiple object tracking: {A} literature review},
	volume = {293},
	issn = {0004-3702},
	shorttitle = {Multiple object tracking},
	url = {https://www.sciencedirect.com/science/article/pii/S0004370220301958},
	doi = {10.1016/j.artint.2020.103448},
	abstract = {Multiple Object Tracking (MOT) has gained increasing attention due to its academic and commercial potential. Although different approaches have been proposed to tackle this problem, it still remains challenging due to factors like abrupt appearance changes and severe object occlusions. In this work, we contribute the first comprehensive and most recent review on this problem. We inspect the recent advances in various aspects and propose some interesting directions for future research. To the best of our knowledge, there has not been any extensive review on this topic in the community. We endeavor to provide a thorough review on the development of this problem in recent decades. The main contributions of this review are fourfold: 1) Key aspects in an MOT system, including formulation, categorization, key principles, evaluation of MOT are discussed; 2) Instead of enumerating individual works, we discuss existing approaches according to various aspects, in each of which methods are divided into different groups and each group is discussed in detail for the principles, advances and drawbacks; 3) We examine experiments of existing publications and summarize results on popular datasets to provide quantitative and comprehensive comparisons. By analyzing the results from different perspectives, we have verified some basic agreements in the field; and 4) We provide a discussion about issues of MOT research, as well as some interesting directions which will become potential research effort in the future.},
	urldate = {2023-11-20},
	journal = {Artificial Intelligence},
	author = {Luo, Wenhan and Xing, Junliang and Milan, Anton and Zhang, Xiaoqin and Liu, Wei and Kim, Tae-Kyun},
	month = apr,
	year = {2021},
	keywords = {Data association, Multi-object tracking, Survey},
	pages = {103448},
}

@article{mengoudi_augmenting_2020,
	title = {Augmenting {Dementia} {Cognitive} {Assessment} {With} {Instruction}-{Less} {Eye}-{Tracking} {Tests}},
	volume = {24},
	issn = {2168-2208},
	url = {https://ieeexplore.ieee.org/abstract/document/9124654?casa_token=sGT3MgzNnXAAAAAA:_fZcXvSClx_IuUAOYy4gwZ_LApJagBjnp8dbKAl1RD67ncVMqRatQa8C0BkU2EF8L5Lm8LMNUoA},
	doi = {10.1109/JBHI.2020.3004686},
	abstract = {Eye-tracking technology is an innovative tool that holds promise for enhancing dementia screening. In this work, we introduce a novel way of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test. Our approach is based on self-supervised representation learning where, by training initially a deep neural network to solve a pretext task using well-defined available labels (e.g. recognising distinct cognitive activities in healthy individuals), the network encodes high-level semantic information which is useful for solving other problems of interest (e.g. dementia classification). Inspired by previous work in explainable AI, we use the Layer-wise Relevance Propagation (LRP) technique to describe our network's decisions in differentiating between the distinct cognitive activities. The extent to which eye-tracking features of dementia patients deviate from healthy behaviour is then explored, followed by a comparison between self-supervised and handcrafted representations on discriminating between participants with and without dementia. Our findings not only reveal novel self-supervised learning features that are more sensitive than handcrafted features in detecting performance differences between participants with and without dementia across a variety of tasks, but also validate that instruction-less eye-tracking tests can detect oculomotor biomarkers of dementia-related cognitive dysfunction. This work highlights the contribution of self-supervised representation learning techniques in biomedical applications where the small number of patients, the non-homogenous presentations of the disease and the complexity of the setting can be a challenge using state-of-the-art feature extraction methods.},
	number = {11},
	urldate = {2023-11-14},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	author = {Mengoudi, Kyriaki and Ravi, Daniele and Yong, Keir X. X. and Primativo, Silvia and Pavisic, Ivanna M. and Brotherhood, Emilie and Lu, Kirsty and Schott, Jonathan M. and Crutch, Sebastian J. and Alexander, Daniel C.},
	month = nov,
	year = {2020},
	note = {Conference Name: IEEE Journal of Biomedical and Health Informatics},
	pages = {3066--3075},
}

@inproceedings{oshea_supervision_2023,
	title = {{SuperVision}: {Self}-{Supervised} {Super}-{Resolution} for {Appearance}-{Based} {Gaze} {Estimation}},
	shorttitle = {{SuperVision}},
	url = {https://openreview.net/forum?id=w5uLSMt3Qc},
	abstract = {Gaze estimation is a valuable tool with a broad range of applications in various fields, including medicine, psychology, virtual reality, marketing, and safety. Therefore, it is essential to have gaze estimation software that is cost-efficient and high-performing. Accurately predicting gaze remains a difficult task, particularly in real-world situations where images are affected by motion blur, video compression, and noise. Super-resolution (SR) has been shown to remove these degradations and improve image quality from a visual perspective. This work examines the usefulness of super-resolution for improving appearance-based gaze estimation and demonstrates that not all SR models preserve the gaze direction. We propose a two-step framework for gaze estimation based on the SwinIR super-resolution model. The proposed method consistently outperforms the state-of-the-art, particularly in scenarios involving low-resolution or degraded images. Furthermore, we examine the use of super-resolution through the lens of self-supervised learning for gaze estimation and propose a novel architecture “SuperVision” by fusing an SR backbone network to a ResNet18. While only using 20{\textbackslash}\% of the data, the proposed SuperVision architecture outperforms the state-of-the-art GazeTR method by 15.5{\textbackslash}\%.},
	language = {en},
	urldate = {2023-11-14},
	author = {O'Shea, Galen and Komeili, Majid},
	month = oct,
	year = {2023},
}

@article{hunt_formalizing_2021,
	title = {Formalizing planning and information search in naturalistic decision-making},
	volume = {24},
	copyright = {2021 Springer Nature America, Inc.},
	issn = {1546-1726},
	url = {https://www.nature.com/articles/s41593-021-00866-w},
	doi = {10.1038/s41593-021-00866-w},
	abstract = {Decisions made by mammals and birds are often temporally extended. They require planning and sampling of decision-relevant information. Our understanding of such decision-making remains in its infancy compared with simpler, forced-choice paradigms. However, recent advances in algorithms supporting planning and information search provide a lens through which we can explain neural and behavioral data in these tasks. We review these advances to obtain a clearer understanding for why planning and curiosity originated in certain species but not others; how activity in the medial temporal lobe, prefrontal and cingulate cortices may support these behaviors; and how planning and information search may complement each other as means to improve future action selection.},
	language = {en},
	number = {8},
	urldate = {2023-11-14},
	journal = {Nature Neuroscience},
	author = {Hunt, L. T. and Daw, N. D. and Kaanders, P. and MacIver, M. A. and Mugan, U. and Procyk, E. and Redish, A. D. and Russo, E. and Scholl, J. and Stachenfeld, K. and Wilson, C. R. E. and Kolling, N.},
	month = aug,
	year = {2021},
	note = {Number: 8
Publisher: Nature Publishing Group},
	keywords = {Decision, Learning algorithms, Motivation, Psychology, Reward},
	pages = {1051--1064},
}

@article{kadner_finding_2023,
	title = {Finding your {Way} {Out}: {Planning} {Strategies} in {Human} {Maze}-{Solving} {Behavior}},
	volume = {45},
	shorttitle = {Finding your {Way} {Out}},
	url = {https://escholarship.org/uc/item/94t1t8kw},
	abstract = {In many situations encountered in our daily lives where we have several options to choose from, we need to balance the amount of planning into the future with the number of alternatives we want to consider to achieve our long-term goals. A popular way to study behavior in these planning problems in controlled environments are maze-solving tasks since they can be precisely defined and controlled in terms of their topology. In our study, participants solved mazes that differed systematically in topological properties regulating the number of alternatives and depth of paths. Replicating previous results, we show the influence of these spatial features on performance and stopping times. Longer and more branched solution paths lead to more planning effort and longer solution times. Additionally, we measured subjects’ eye movements to investigate their planning horizon. Our results suggest that people decrease their planning depth with increasing number of alternatives.},
	language = {en},
	number = {45},
	urldate = {2023-11-14},
	journal = {Proceedings of the Annual Meeting of the Cognitive Science Society},
	author = {Kadner, Florian and Willkomm, Hannah and Ibs, Inga and Rothkopf, Constantin},
	year = {2023},
}

@inproceedings{li_modeling_2023,
	title = {Modeling {Human} {Eye} {Movements} with {Neural} {Networks} in a {Maze}-{Solving} {Task}},
	url = {https://proceedings.mlr.press/v210/li23a.html},
	abstract = {From smoothly pursuing moving objects to rapidly shifting gazes during visual search, humans employ a wide variety of eye movement strategies in different contexts. While eye movements provide a rich window into mental processes, building generative models of eye movements is notoriously difficult, and to date the computational objectives guiding eye movements remain largely a mystery. In this work, we tackled these problems in the context of a canonical spatial planning task, maze-solving. We collected eye movement data from human subjects and built deep generative models of eye movements using a novel differentiable architecture for gaze fixations and gaze shifts. We found that human eye movements are best predicted by a model that is optimized not to perform the task as efficiently as possible but instead to run an internal simulation of an object traversing the maze. This not only provides a generative model of eye movements in this task but also suggests a computational theory for how humans solve the task, namely that humans use mental simulation.},
	language = {en},
	urldate = {2023-11-14},
	booktitle = {Proceedings of {The} 1st {Gaze} {Meets} {ML} workshop},
	publisher = {PMLR},
	author = {Li, Jason and Watters, Nicholas and Sohn, Hansem and Jazayeri, Mehrdad},
	month = apr,
	year = {2023},
	note = {ISSN: 2640-3498},
	pages = {98--112},
}

@inproceedings{banerjee_attention-based_2023,
	title = {An {Attention}-based {Predictive} {Agent} for {Handwritten} {Numeral}/{Alphabet} {Recognition} via {Generation}},
	url = {https://openreview.net/forum?id=v25W8STO2T},
	abstract = {A number of attention-based models for either classification or generation of handwritten numerals/alphabets have been reported in the literature. However, generation and classification are done jointly in very few end-to-end models. We propose a predictive agent model that actively samples its visual environment via a sequence of glimpses. The attention is driven by the agent's sensory prediction (or generation) error. At each sampling instant, the model predicts the observation class and completes the partial sequence observed till that instant. It learns where and what to sample by jointly minimizing the classification and generation errors. Three variants of this model are evaluated for handwriting generation and recognition on images of handwritten numerals and alphabets from benchmark datasets. We show that the proposed model is more efficient in handwritten numeral/alphabet recognition than human participants in a recently published study as well as a highly-cited attention-based reinforcement model. This is the first known attention-based agent to interact with and learn end-to-end from images for recognition via generation, with high degree of accuracy and efficiency.},
	language = {en},
	urldate = {2023-11-14},
	author = {Banerjee, Bonny and Baruah, Murchana},
	month = oct,
	year = {2023},
}

@article{patney_towards_2016,
	title = {Towards foveated rendering for gaze-tracked virtual reality},
	volume = {35},
	issn = {0730-0301},
	url = {https://doi.org/10.1145/2980179.2980246},
	doi = {10.1145/2980179.2980246},
	abstract = {Foveated rendering synthesizes images with progressively less detail outside the eye fixation region, potentially unlocking significant speedups for wide field-of-view displays, such as head mounted displays, where target framerate and resolution is increasing faster than the performance of traditional real-time renderers. To study and improve potential gains, we designed a foveated rendering user study to evaluate the perceptual abilities of human peripheral vision when viewing today's displays. We determined that filtering peripheral regions reduces contrast, inducing a sense of tunnel vision. When applying a postprocess contrast enhancement, subjects tolerated up to 2× larger blur radius before detecting differences from a non-foveated ground truth. After verifying these insights on both desktop and head mounted displays augmented with high-speed gaze-tracking, we designed a perceptual target image to strive for when engineering a production foveated renderer. Given our perceptual target, we designed a practical foveated rendering system that reduces number of shades by up to 70\% and allows coarsened shading up to 30° closer to the fovea than Guenter et al. [2012] without introducing perceivable aliasing or blur. We filter both pre- and post-shading to address aliasing from undersampling in the periphery, introduce a novel multiresolution- and saccade-aware temporal antialising algorithm, and use contrast enhancement to help recover peripheral details that are resolvable by our eye but degraded by filtering. We validate our system by performing another user study. Frequency analysis shows our system closely matches our perceptual target. Measurements of temporal stability show we obtain quality similar to temporally filtered non-foveated renderings.},
	number = {6},
	urldate = {2023-11-14},
	journal = {ACM Transactions on Graphics},
	author = {Patney, Anjul and Salvi, Marco and Kim, Joohwan and Kaplanyan, Anton and Wyman, Chris and Benty, Nir and Luebke, David and Lefohn, Aaron},
	month = dec,
	year = {2016},
	keywords = {foveated rendering, gaze-tracking, perception, virtual reality},
	pages = {179:1--179:12},
}

@inproceedings{beymer_webgazeanalyzer_2005,
	address = {New York, NY, USA},
	series = {{CHI} {EA} '05},
	title = {{WebGazeAnalyzer}: a system for capturing and analyzing web reading behavior using eye gaze},
	isbn = {978-1-59593-002-6},
	shorttitle = {{WebGazeAnalyzer}},
	url = {https://doi.org/10.1145/1056808.1057055},
	doi = {10.1145/1056808.1057055},
	abstract = {Capturing and analyzing the detailed eye movements of a user while reading a web page can reveal much about the ways in which web reading occurs. The WebGazeAnalyzer system described here is a remote-camera system, requiring no invasive head-mounted apparatus, giving test subjects a normal web use experience when performing web-based tasks. While many such systems have been used in the past to collect eye gaze data, WebGazeAnalyzer brings together several techniques for efficiently collecting, analyzing and re-analyzing eye gaze data. We briefly describe techniques for overcoming the inherent inaccuracies of such apparatus, illustrating how we capture and analyze eye gaze data for commercial web design problems. Techniques developed here include methods to group fixations along lines of text, and reading analysis to measure reading speed, regressions, and coverage of web page text.},
	urldate = {2023-11-14},
	booktitle = {{CHI} '05 {Extended} {Abstracts} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Beymer, David and Russell, Daniel M.},
	month = apr,
	year = {2005},
	keywords = {data analysis methods, eye gaze, eye tracking, gaze registration, reading detection},
	pages = {1913--1916},
}

@article{li_appearance-based_2019,
	title = {Appearance-{Based} {Gaze} {Estimator} for {Natural} {Interaction} {Control} of {Surgical} {Robots}},
	volume = {7},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/abstract/document/8648437},
	doi = {10.1109/ACCESS.2019.2900424},
	abstract = {Robots are playing an increasingly important role in modern surgery. However, conventional human-computer interaction methods, such as joystick control and sound control, have some shortcomings, and medical personnel are required to specifically practice operating the robot. We propose a human-computer interaction model based on eye movement with which medical staff can conveniently use their eye movements to control the robot. Our algorithm requires only an RGB camera to perform tasks without requiring expensive eye-tracking devices. Two kinds of eye control modes are designed in this paper. The first type is the pick and place movement, with which the user uses eye gaze to specify the point where the robotic arm is required to move. The second type is user command movement, with which the user can use eye gaze to select the direction in which the user desires the robot to move. The experimental results demonstrate the feasibility and convenience of these two modes of movement.},
	urldate = {2023-11-14},
	journal = {IEEE Access},
	author = {Li, Peng and Hou, Xuebin and Duan, Xingguang and Yip, Hiuman and Song, Guoli and Liu, Yunhui},
	year = {2019},
	note = {Conference Name: IEEE Access},
	pages = {25095--25110},
}

@misc{noauthor_appearance-based_nodate,
	title = {Appearance-{Based} {Gaze} {Estimator} for {Natural} {Interaction} {Control} of {Surgical} {Robots} {\textbar} {IEEE} {Journals} \& {Magazine} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/abstract/document/8648437},
	urldate = {2023-11-14},
}

@inproceedings{zhang_evaluation_2019,
	address = {New York, NY, USA},
	series = {{CHI} '19},
	title = {Evaluation of {Appearance}-{Based} {Methods} and {Implications} for {Gaze}-{Based} {Applications}},
	isbn = {978-1-4503-5970-2},
	url = {https://doi.org/10.1145/3290605.3300646},
	doi = {10.1145/3290605.3300646},
	abstract = {Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction.},
	urldate = {2023-11-14},
	booktitle = {Proceedings of the 2019 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Zhang, Xucong and Sugano, Yusuke and Bulling, Andreas},
	month = may,
	year = {2019},
	keywords = {appearance-based gaze estimation, model-based gaze estimation, opengaze, software toolkit, tobii eyex},
	pages = {1--13},
}

@inproceedings{lakshminarasimhan_planning_2023,
	title = {Planning by {Active} {Sensing}},
	url = {https://openreview.net/forum?id=kMNLseFKcT},
	abstract = {Flexible behavior requires rapid planning, but planning requires a good internal model of the environment. Learning this model by trial-and-error is impractical when acting in complex environments. How do humans plan action sequences efficiently when there is uncertainty about model components? To address this, we asked human participants to navigate complex mazes in virtual reality. We found that the paths taken to gather rewards were close to optimal even though participants had no prior knowledge of these environments. Based on the sequential eye movement patterns observed when participants mentally compute a path before navigating, we develop an algorithm that is capable of rapidly planning under uncertainty by active sensing i.e., visually sampling information about the structure of the environment. ew eye movements are chosen in an iterative manner by following the gradient of a dynamic value map which is updated based on the previous eye movement, until the planning process reaches convergence. In addition to bearing hallmarks of human navigational planning, the proposed algorithm is sample-efficient such that the number of visual samples needed for planning scales linearly with the path length regardless of the size of the state space.},
	language = {en},
	urldate = {2023-11-14},
	author = {Lakshminarasimhan, Kaushik and Zhu, Seren and Angelaki, Dora},
	month = oct,
	year = {2023},
}

@inproceedings{singh_eg-sif_2023,
	title = {{EG}-{SIF}: {Improving} {Appearance} {Based} {Gaze} {Estimation} using {Self} {Improving} {Features}},
	shorttitle = {{EG}-{SIF}},
	url = {https://openreview.net/forum?id=tY06Zwn3v4},
	abstract = {Accurate gaze estimation is integral to a myriad of applications, from augmented reality to non-verbal communication analysis. However, the performance of gaze estimation models is often compromised by adverse conditions such as poor lighting, artifacts, low-resolution imagery, etc. To counter these challenges, we introduce the Eye Gaze Estimation with Self- Improving Features (EG-SIF) method, a novel approach that enhances model robustness and performance in suboptimal conditions. The EG-SIF method innovatively segregates eye images by quality, synthesizing pairs of high-quality and corresponding degraded images. It leverages a multitask training paradigm that emphasizes image enhancement through reconstruction from impaired versions. This strategy is not only pioneering in the realm of data segregation based on image quality but also introduces a transformative multitask framework that integrates image enhancement as an auxiliary task. We implement adaptive binning and mixed regression with intermediate supervision to refine our model’s capability further. Empirical evidence demonstrates that our EG-SIF method significantly reduces the angular error in gaze estimation on challenging datasets such as MPIIGaze, improving from 4.64 to 4.53, and on RTGene, from 7.44 to 7.41, thereby setting a new benchmark in the field. Our contributions lay the foundation for future gaze estimation models that can operate reliably despite the presence of image quality adversities.},
	language = {en},
	urldate = {2023-11-14},
	author = {Singh, Vasudev and Langde, Chaitanya and Lakotia, Sourav and Kannan, Vignesh and Ahmed, Shuaib},
	month = oct,
	year = {2023},
}

@article{lovett_selection_2019,
	title = {Selection enables enhancement: {An} integrated model of object tracking},
	volume = {19},
	issn = {1534-7362},
	shorttitle = {Selection enables enhancement},
	url = {https://doi.org/10.1167/19.14.23},
	doi = {10.1167/19.14.23},
	abstract = {The diversity of research on visual attention and multiple-object tracking presents challenges for anyone hoping to develop a unified account. One key challenge is identifying the attentional limitations that give rise to competition among targets during tracking. To address this challenge, we present a computational model of object tracking that relies on two attentional mechanisms: serial selection and parallel enhancement. Selection picks out an object for further processing, whereas enhancement increases sensitivity to stimuli in regions where objects have been selected previously. In this model, multiple target locations can be tracked in parallel via enhancement, whereas a single target can be selected so that additional information beyond its location can be processed. In simulations of two psychological experiments, we demonstrate that spatial competition during enhancement and temporal competition for selection can explain a range of findings on multiple-object tracking, and we argue that the interaction between selection and enhancement captured in the model is critical to understanding attention more broadly.},
	number = {14},
	urldate = {2023-11-08},
	journal = {Journal of Vision},
	author = {Lovett, Andrew and Bridewell, Will and Bello, Paul},
	month = dec,
	year = {2019},
	pages = {23},
}

@article{zhong_why_2014,
	title = {Why do people appear not to extrapolate trajectories during multiple object tracking? {A} computational investigation},
	volume = {14},
	issn = {1534-7362},
	shorttitle = {Why do people appear not to extrapolate trajectories during multiple object tracking?},
	url = {https://doi.org/10.1167/14.12.12},
	doi = {10.1167/14.12.12},
	abstract = {Intuitively, extrapolating object trajectories should make visual tracking more accurate.  This has proven to be true in many contexts that involve tracking a single item. But surprisingly, when tracking multiple identical items in what is known as “multiple object tracking,” observers often appear to ignore direction of motion, relying instead on basic spatial memory. We investigated potential reasons for this behavior through probabilistic models that were endowed with perceptual limitations in the range of typical human observers, including noisy spatial perception. When we compared a model that weights its extrapolations relative to other sources of information about object position, and one that does not extrapolate at all, we found no reliable difference in performance, belying the intuition that extrapolation always benefits tracking. In follow-up experiments we found this to be true for a variety of models that weight observations and predictions in different ways; in some cases we even observed worse performance for models that use extrapolations compared to a model that does not at all. Ultimately, the best performing models either did not extrapolate, or extrapolated very conservatively, relying heavily on observations. These results illustrate the difficulty and attendant hazards of using noisy inputs to extrapolate the trajectories of multiple objects simultaneously in situations with targets and featurally confusable nontargets.},
	number = {12},
	urldate = {2023-11-08},
	journal = {Journal of Vision},
	author = {Zhong, Sheng-hua and Ma, Zheng and Wilson, Colin and Liu, Yan and Flombaum, Jonathan I},
	month = oct,
	year = {2014},
	pages = {12},
}

@inproceedings{li_multiple_2010,
	title = {A multiple object tracking method using {Kalman} filter},
	url = {https://ieeexplore.ieee.org/document/5512258},
	doi = {10.1109/ICINFA.2010.5512258},
	abstract = {It is important to maintain the identity of multiple targets while tracking them in some applications such as behavior understanding. However, unsatisfying tracking results may be produced due to different real-time conditions. These conditions include: inter-object occlusion, occlusion of the ocjects by background obstacles, splits and merges, which are observed when objects are being tracked in real-time. In this paper, an algorithm of feature-based using Kalman filter motion to handle multiple objects tracking is proposed. The system is fully automatic and requires no manual input of any kind for initialization of tracking. Through establishing Kalman filter motion model with the features centroid and area of moving objects in a single fixed camera monitoring scene, using information obtained by detection to judge whether merge or split occurred, the calculation of the cost function can be used to solve the problems of correspondence after split happened. The algorithm proposed is validated on human and vehicle image sequence. The results shows that the algorithm proposed achieves efficient tracking of multiple moving objects under the confusing situations.},
	urldate = {2023-11-08},
	booktitle = {The 2010 {IEEE} {International} {Conference} on {Information} and {Automation}},
	author = {Li, Xin and Wang, Kejun and Wang, Wei and Li, Yang},
	month = jun,
	year = {2010},
	pages = {1862--1866},
}

@article{vondrick_tracking_2018,
	title = {Tracking {Emerges} by {Colorizing} {Videos}},
	url = {http://arxiv.org/abs/1806.09594},
	abstract = {We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-scale videos by copying colors from a reference frame. Quantitative and qualitative experiments suggest that this task causes the model to automatically learn to track visual regions. Although the model is trained without any ground-truth labels, our method learns to track well enough to outperform the latest methods based on optical flow. Moreover, our results suggest that failures to track are correlated with failures to colorize, indicating that advancing video colorization may further improve self-supervised visual tracking.},
	urldate = {2020-11-25},
	journal = {arXiv:1806.09594 [cs]},
	author = {Vondrick, Carl and Shrivastava, Abhinav and Fathi, Alireza and Guadarrama, Sergio and Murphy, Kevin},
	month = jul,
	year = {2018},
	note = {arXiv: 1806.09594},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Robotics},
}

@article{guo_attention_2022,
	title = {Attention mechanisms in computer vision: {A} survey},
	volume = {8},
	issn = {2096-0662},
	shorttitle = {Attention mechanisms in computer vision},
	url = {https://doi.org/10.1007/s41095-022-0271-y},
	doi = {10.1007/s41095-022-0271-y},
	abstract = {Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentionsis dedicated to collecting related work. We also suggest future directions for attention mechanism research.},
	language = {en},
	number = {3},
	urldate = {2023-11-08},
	journal = {Computational Visual Media},
	author = {Guo, Meng-Hao and Xu, Tian-Xing and Liu, Jiang-Jiang and Liu, Zheng-Ning and Jiang, Peng-Tao and Mu, Tai-Jiang and Zhang, Song-Hai and Martin, Ralph R. and Cheng, Ming-Ming and Hu, Shi-Min},
	month = sep,
	year = {2022},
	keywords = {attention, computer vision, deep learning, salience, transformer},
	pages = {331--368},
}

@inproceedings{wang_towards_2020,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Towards {Real}-{Time} {Multi}-{Object} {Tracking}},
	isbn = {978-3-030-58621-8},
	doi = {10.1007/978-3-030-58621-8_7},
	abstract = {Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed might lead to efficiency problems, as the running time is simply a sum of the two steps without investigating potential structures that can be shared between them. Existing research efforts on real-time MOT usually focus on the association step, so they are essentially real-time association methods but not real-time MOT system. In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model. Specifically, we incorporate the appearance embedding model into a single-shot detector, such that the model can simultaneously output detections and the corresponding embeddings. We further propose a simple and fast association method that works in conjunction with the joint model. In both components the computation cost is significantly reduced compared with former MOT systems, resulting in a neat and fast baseline for future follow-ups on real-time MOT algorithm design. To our knowledge, this work reports the first (near) real-time MOT system, with a running speed of 22 to 40 FPS depending on the input resolution. Meanwhile, its tracking accuracy is comparable to the state-of-the-art trackers embodying separate detection and embedding (SDE) learning (\$\$64.4{\textbackslash}\%\$\$64.4\%MOTA v.s.  \$\$66.1{\textbackslash}\%\$\$66.1\%MOTA on MOT-16 challenge). Code and models are available at https://github.com/Zhongdao/Towards-Realtime-MOT.},
	language = {en},
	booktitle = {Computer {Vision} – {ECCV} 2020},
	publisher = {Springer International Publishing},
	author = {Wang, Zhongdao and Zheng, Liang and Liu, Yixuan and Li, Yali and Wang, Shengjin},
	editor = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
	year = {2020},
	keywords = {Multi-Object Tracking},
	pages = {107--122},
}

@inproceedings{kosiorek_sequential_2018,
	title = {Sequential {Attend}, {Infer}, {Repeat}: {Generative} {Modelling} of {Moving} {Objects}},
	volume = {31},
	shorttitle = {Sequential {Attend}, {Infer}, {Repeat}},
	url = {https://proceedings.neurips.cc/paper_files/paper/2018/hash/7417744a2bac776fabe5a09b21c707a2-Abstract.html},
	abstract = {We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for image sequences.
It can reliably discover and track objects through the sequence; it can also conditionally generate future frames, thereby simulating expected motion of objects. 
This is achieved by explicitly encoding object numbers, locations and appearances in the latent variables of the model.
SQAIR retains all strengths of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al. 2016), including unsupervised learning, made possible by inductive biases present in the model structure.
We use a moving multi-{\textbackslash}textsc\{mnist\} dataset to show limitations of AIR in detecting overlapping or partially occluded objects, and show how {\textbackslash}textsc\{sqair\} overcomes them by leveraging temporal consistency of objects.
Finally, we also apply SQAIR to real-world pedestrian CCTV data, where it learns to reliably detect, track and generate walking pedestrians with no supervision.},
	urldate = {2023-11-08},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Kosiorek, Adam and Kim, Hyunjik and Teh, Yee Whye and Posner, Ingmar},
	year = {2018},
}

@inproceedings{eslami_attend_2016,
	title = {Attend, {Infer}, {Repeat}: {Fast} {Scene} {Understanding} with {Generative} {Models}},
	volume = {29},
	shorttitle = {Attend, {Infer}, {Repeat}},
	url = {https://proceedings.neurips.cc/paper/2016/hash/52947e0ade57a09e4a1386d08f17b656-Abstract.html},
	abstract = {We present a framework for efficient inference in structured image models that explicitly reason about objects. We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time. Crucially, the model itself learns to choose the appropriate number of inference steps. We use this scheme to learn to perform inference in partially specified 2D models (variable-sized variational auto-encoders) and fully specified 3D models (probabilistic renderers). We show that such models learn to identify multiple objects - counting, locating and classifying the elements of a scene - without any supervision, e.g., decomposing 3D images with various numbers of objects in a single forward pass of a neural network at unprecedented speed. We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization.},
	urldate = {2023-11-08},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Eslami, S. M. Ali and Heess, Nicolas and Weber, Theophane and Tassa, Yuval and Szepesvari, David and kavukcuoglu, koray and Hinton, Geoffrey E},
	year = {2016},
}

@inproceedings{xu_how_2020,
	title = {How to {Train} {Your} {Deep} {Multi}-{Object} {Tracker}},
	url = {https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_How_to_Train_Your_Deep_Multi-Object_Tracker_CVPR_2020_paper.html},
	urldate = {2023-09-05},
	author = {Xu, Yihong and Osep, Aljosa and Ban, Yutong and Horaud, Radu and Leal-Taixe, Laura and Alameda-Pineda, Xavier},
	year = {2020},
	pages = {6787--6796},
}

@article{kothari_gaze--wild_2020,
	title = {Gaze-in-wild: {A} dataset for studying eye and head coordination in everyday activities},
	volume = {10},
	copyright = {2020 The Author(s)},
	issn = {2045-2322},
	shorttitle = {Gaze-in-wild},
	url = {https://www.nature.com/articles/s41598-020-59251-5},
	doi = {10.1038/s41598-020-59251-5},
	abstract = {The study of gaze behavior has primarily been constrained to controlled environments in which the head is fixed. Consequently, little effort has been invested in the development of algorithms for the categorization of gaze events (e.g. fixations, pursuits, saccade, gaze shifts) while the head is free, and thus contributes to the velocity signals upon which classification algorithms typically operate. Our approach was to collect a novel, naturalistic, and multimodal dataset of eye + head movements when subjects performed everyday tasks while wearing a mobile eye tracker equipped with an inertial measurement unit and a 3D stereo camera. This Gaze-in-the-Wild dataset (GW) includes eye + head rotational velocities (deg/s), infrared eye images and scene imagery (RGB + D). A portion was labelled by coders into gaze motion events with a mutual agreement of 0.74 sample based Cohen’s κ. This labelled data was used to train and evaluate two machine learning algorithms, Random Forest and a Recurrent Neural Network model, for gaze event classification. Assessment involved the application of established and novel event based performance metrics. Classifiers achieve {\textasciitilde}87\% human performance in detecting fixations and saccades but fall short (50\%) on detecting pursuit movements. Moreover, pursuit classification is far worse in the absence of head movement information. A subsequent analysis of feature significance in our best performing model revealed that classification can be done using only the magnitudes of eye and head movements, potentially removing the need for calibration between the head and eye tracking systems. The GW dataset, trained classifiers and evaluation metrics will be made publicly available with the intention of facilitating growth in the emerging area of head-free gaze event classification.},
	language = {en},
	number = {1},
	urldate = {2023-11-08},
	journal = {Scientific Reports},
	author = {Kothari, Rakshit and Yang, Zhizhuo and Kanan, Christopher and Bailey, Reynold and Pelz, Jeff B. and Diaz, Gabriel J.},
	month = feb,
	year = {2020},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Navigation, Oculomotor system, Smooth pursuit, Vestibuloocular reflex},
	pages = {2539},
}

@article{matthis_gaze_2018,
	title = {Gaze and the {Control} of {Foot} {Placement} {When} {Walking} in {Natural} {Terrain}},
	volume = {28},
	issn = {0960-9822},
	url = {https://www.sciencedirect.com/science/article/pii/S0960982218303099},
	doi = {10.1016/j.cub.2018.03.008},
	abstract = {Human locomotion through natural environments requires precise coordination between the biomechanics of the bipedal gait cycle and the eye movements that gather the information needed to guide foot placement. However, little is known about how the visual and locomotor systems work together to support movement through the world. We developed a system to simultaneously record gaze and full-body kinematics during locomotion over different outdoor terrains. We found that not only do walkers tune their gaze behavior to the specific information needed to traverse paths of varying complexity but that they do so while maintaining a constant temporal look-ahead window across all terrains. This strategy allows walkers to use gaze to tailor their energetically optimal preferred gait cycle to the upcoming path in order to balance between the drive to move efficiently and the need to place the feet in stable locations. Eye movements and locomotion are intimately linked in a way that reflects the integration of energetic costs, environmental uncertainty, and momentary informational demands of the locomotor task. Thus, the relationship between gaze and gait reveals the structure of the sensorimotor decisions that support successful performance in the face of the varying demands of the natural world.
Video Abstract},
	number = {8},
	urldate = {2023-11-08},
	journal = {Current Biology},
	author = {Matthis, Jonathan Samir and Yates, Jacob L. and Hayhoe, Mary M.},
	month = apr,
	year = {2018},
	keywords = {eye movements, foot placement, gaze, locomotion, real-world, rough terrain, walking},
	pages = {1224--1233.e5},
}

@article{chen_coco-search18_2021,
	title = {{COCO}-{Search18} fixation dataset for predicting goal-directed attention control},
	volume = {11},
	copyright = {2021 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-021-87715-9},
	doi = {10.1038/s41598-021-87715-9},
	abstract = {Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control – saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior large enough to train deep-network models. We collected eye-movement behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding \$\${\textbackslash}sim\$\$300,000 search fixations. We thoroughly characterize COCO-Search18, and benchmark it using three machine-learning methods: a ResNet50 object detector, a ResNet50 trained on fixation-density maps, and an inverse-reinforcement-learning model trained on behavioral search scanpaths. Models were also trained/tested on images transformed to approximate a foveated retina, a fundamental biological constraint. These models, each having a different reliance on behavioral training, collectively comprise the new state-of-the-art in predicting goal-directed search fixations. Our expectation is that future work using COCO-Search18 will far surpass these initial efforts, finding applications in domains ranging from human-computer interactive systems that can anticipate a person’s intent and render assistance to the potentially early identification of attention-related clinical disorders (ADHD, PTSD, phobia) based on deviation from neurotypical fixation behavior.},
	language = {en},
	number = {1},
	urldate = {2023-11-08},
	journal = {Scientific Reports},
	author = {Chen, Yupei and Yang, Zhibo and Ahn, Seoyoung and Samaras, Dimitris and Hoai, Minh and Zelinsky, Gregory},
	month = apr,
	year = {2021},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Human behaviour, Psychology},
	pages = {8776},
}

@inproceedings{xu_gaze_2018,
	title = {Gaze {Prediction} in {Dynamic} 360° {Immersive} {Videos}},
	url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Xu_Gaze_Prediction_in_CVPR_2018_paper.html},
	urldate = {2023-11-08},
	author = {Xu, Yanyu and Dong, Yanbing and Wu, Junru and Sun, Zhengzhong and Shi, Zhiru and Yu, Jingyi and Gao, Shenghua},
	year = {2018},
	pages = {5333--5342},
}

@inproceedings{tangemann_measuring_2020,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Measuring the {Importance} of {Temporal} {Features} in {Video} {Saliency}},
	isbn = {978-3-030-58604-1},
	doi = {10.1007/978-3-030-58604-1_40},
	abstract = {Where people look when watching videos is believed to be heavily influenced by temporal patterns. In this work, we test this assumption by quantifying to which extent gaze on recent video saliency benchmarks can be predicted by a static baseline model. On the recent LEDOV dataset, we find that at least 75\% of the explainable information as defined by a gold standard model can be explained using static features. Our baseline model “DeepGaze MR” even outperforms state-of-the-art video saliency models, despite deliberately ignoring all temporal patterns. Visual inspection of our static baseline’s failure cases shows that clear temporal effects on human gaze placement exist, but are both rare in the dataset and not captured by any of the recent video saliency models. To focus the development of video saliency models on better capturing temporal effects we construct a meta-dataset consisting of those examples requiring temporal information.},
	language = {en},
	booktitle = {Computer {Vision} – {ECCV} 2020},
	publisher = {Springer International Publishing},
	author = {Tangemann, Matthias and Kümmerer, Matthias and Wallis, Thomas S. A. and Bethge, Matthias},
	editor = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
	year = {2020},
	keywords = {Gaze prediction, Model evaluation, Saliency, Temporal modelling, Video},
	pages = {667--684},
}

@incollection{vul_explaining_2009,
	title = {Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model},
	url = {http://papers.nips.cc/paper/3828-explaining-human-multiple-object-tracking-as-resource-constrained-approximate-inference-in-a-dynamic-probabilistic-model.pdf},
	urldate = {2019-07-24},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 22},
	publisher = {Curran Associates, Inc.},
	author = {Vul, Ed and Alvarez, George and Tenenbaum, Joshua B. and Black, Michael J.},
	editor = {Bengio, Y. and Schuurmans, D. and Lafferty, J. D. and Williams, C. K. I. and Culotta, A.},
	year = {2009},
	pages = {1955--1963},
}

@article{kummerer_predicting_2023,
	title = {Predicting {Visual} {Fixations}},
	volume = {9},
	url = {https://doi.org/10.1146/annurev-vision-120822-072528},
	doi = {10.1146/annurev-vision-120822-072528},
	abstract = {As we navigate and behave in the world, we are constantly deciding, a few times per second, where to look next. The outcomes of these decisions in response to visual input are comparatively easy to measure as trajectories of eye movements, offering insight into many unconscious and conscious visual and cognitive processes. In this article, we review recent advances in predicting where we look. We focus on evaluating and comparing models: How can we consistently measure how well models predict eye movements, and how can we judge the contribution of different mechanisms? Probabilistic models facilitate a unified approach to fixation prediction that allows us to use explainable information explained to compare different models across different settings, such as static and video saliency, as well as scanpath prediction. We review how the large variety of saliency maps and scanpath models can be translated into this unifying framework, how much different factors contribute, and how we can select the most informative examples for model comparison. We conclude that the universal scale of information gain offers a powerful tool for the inspection of candidate mechanisms and experimental design that helps us understand the continual decision-making process that determines where we look.},
	number = {1},
	urldate = {2023-11-08},
	journal = {Annual Review of Vision Science},
	author = {Kümmerer, Matthias and Bethge, Matthias},
	year = {2023},
	pmid = {37419107},
	note = {\_eprint: https://doi.org/10.1146/annurev-vision-120822-072528},
	keywords = {benchmarking, eye movements, fixations, information theory, model comparison, saliency, taxonomy, transfer learning, unifying framework},
	pages = {269--291},
}

@misc{zeng_motr_2022,
	title = {{MOTR}: {End}-to-{End} {Multiple}-{Object} {Tracking} with {Transformer}},
	shorttitle = {{MOTR}},
	url = {http://arxiv.org/abs/2105.03247},
	abstract = {Temporal modeling of objects is a key challenge in multipleobject tracking (MOT). Existing methods track by associating detections through motion-based and appearance-based similarity heuristics. The post-processing nature of association prevents end-to-end exploitation of temporal variations in video sequence.},
	language = {en},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Zeng, Fangao and Dong, Bin and Zhang, Yuang and Wang, Tiancai and Zhang, Xiangyu and Wei, Yichen},
	month = jul,
	year = {2022},
	note = {arXiv:2105.03247 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{sun_transtrack_2021,
	title = {{TransTrack}: {Multiple} {Object} {Tracking} with {Transformer}},
	shorttitle = {{TransTrack}},
	url = {http://arxiv.org/abs/2012.15460},
	abstract = {In this work, we propose TransTrack, a simple but efﬁcient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set of learned object queries to enable detecting new-coming objects. It builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot, simplifying complicated multi-step settings in tracking-by-detection methods. On MOT17 and MOT20 benchmark, TransTrack achieves 74.5\% and 64.5\% MOTA, respectively, competitive to the state-of-the-art methods. We expect TransTrack to provide a novel perspective for multiple object tracking. The code is available at: https: //github.com/PeizeSun/TransTrack.},
	language = {en},
	urldate = {2023-11-08},
	publisher = {arXiv},
	author = {Sun, Peize and Cao, Jinkun and Jiang, Yi and Zhang, Rufeng and Xie, Enze and Yuan, Zehuan and Wang, Changhu and Luo, Ping},
	month = may,
	year = {2021},
	note = {arXiv:2012.15460 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@inproceedings{meinhardt_trackformer_2022,
	address = {New Orleans, LA, USA},
	title = {{TrackFormer}: {Multi}-{Object} {Tracking} with {Transformers}},
	isbn = {978-1-66546-946-3},
	shorttitle = {{TrackFormer}},
	url = {https://ieeexplore.ieee.org/document/9879668/},
	doi = {10.1109/CVPR52688.2022.00864},
	language = {en},
	urldate = {2023-11-08},
	booktitle = {2022 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Meinhardt, Tim and Kirillov, Alexander and Leal-Taixe, Laura and Feichtenhofer, Christoph},
	month = jun,
	year = {2022},
	pages = {8834--8844},
}

@article{eslami_neural_2018,
	title = {Neural scene representation and rendering},
	volume = {360},
	issn = {0036-8075, 1095-9203},
	url = {https://www.science.org/doi/10.1126/science.aar6170},
	doi = {10.1126/science.aar6170},
	abstract = {A scene-internalizing computer program
            
              To train a computer to “recognize” elements of a scene supplied by its visual sensors, computer scientists typically use millions of images painstakingly labeled by humans. Eslami
              et al.
              developed an artificial vision system, dubbed the Generative Query Network (GQN), that has no need for such labeled data. Instead, the GQN first uses images taken from different viewpoints and creates an abstract description of the scene, learning its essentials. Next, on the basis of this representation, the network predicts what the scene would look like from a new, arbitrary viewpoint.
            
            
              Science
              , this issue p.
              1204
            
          , 
            A computer vision system predicts how a 3D scene looks from any viewpoint after just a few 2D views from other viewpoints.
          , 
            Scene representation—the process of converting visual sensory data into concise descriptions—is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.},
	language = {en},
	number = {6394},
	urldate = {2023-11-07},
	journal = {Science},
	author = {Eslami, S. M. Ali and Jimenez Rezende, Danilo and Besse, Frederic and Viola, Fabio and Morcos, Ari S. and Garnelo, Marta and Ruderman, Avraham and Rusu, Andrei A. and Danihelka, Ivo and Gregor, Karol and Reichert, David P. and Buesing, Lars and Weber, Theophane and Vinyals, Oriol and Rosenbaum, Dan and Rabinowitz, Neil and King, Helen and Hillier, Chloe and Botvinick, Matt and Wierstra, Daan and Kavukcuoglu, Koray and Hassabis, Demis},
	month = jun,
	year = {2018},
	pages = {1204--1210},
}

@article{srivastava_attention_2016,
	title = {Attention {Modulates} {Spatial} {Precision} in {Multiple}‐{Object} {Tracking}},
	volume = {8},
	issn = {1756-8757, 1756-8765},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/tops.12189},
	doi = {10.1111/tops.12189},
	abstract = {We present a computational model of multiple-object tracking that makes trial-level predictions about the allocation of visual attention and the effect of this allocation on observers’ ability to track multiple objects simultaneously. This model follows the intuition that increased attention to a location increases the spatial resolution of its internal representation. Using a combination of empirical and computational experiments, we demonstrate the existence of a tight coupling between cognitive and perceptual resources in this task: Low-level tracking of objects generates bottom-up predictions of error likelihood, and high-level attention allocation selectively reduces error probabilities in attended locations while increasing it at non-attended locations. Whereas earlier models of multiple-object tracking have predicted the big picture relationship between stimulus complexity and response accuracy, our approach makes accurate predictions of both the macro-scale effect of target number and velocity on tracking difﬁculty and micro-scale variations in difﬁculty across individual trials and targets arising from the idiosyncratic within-trial interactions of targets and distractors.},
	language = {en},
	number = {1},
	urldate = {2023-11-07},
	journal = {Topics in Cognitive Science},
	author = {Srivastava, Nisheeth and Vul, Ed},
	month = jan,
	year = {2016},
	pages = {335--348},
}

@article{zhou_topological_2010,
	title = {Topological change disturbs object continuity in attentive tracking},
	volume = {107},
	url = {https://www.pnas.org/content/107/50/21920},
	doi = {10.1073/pnas.1010919108},
	abstract = {The question of what is a perceptual object is one of the most central and also controversial issues in cognitive science. According to the topological approach to perceptual organization, the core intuitive notion of an object—the holistic identity preserved over shape-changing transformations—may be characterized precisely as topological invariance. Here we show that, across a series of multiple-object tracking tasks, performance was not disrupted when the moving items underwent massive featural changes. However, performance was significantly impaired when the items changed their topological properties of holes, demonstrating that topological invariance constrains what counts as an object in the first place. Consistent with previous findings, fMRI studies indicated that the anterior temporal lobe may be involved in the formation of object representation defined by topological constraints.},
	number = {50},
	urldate = {2020-12-21},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Zhou, Ke and Luo, Huan and Zhou, Tiangang and Zhuo, Yan and Chen, Lin},
	month = dec,
	year = {2010},
	keywords = {attention, global-first, object-based, topological definition of objects, topological transformation},
	pages = {21920--21924},
}

@incollection{vul_explaining_2009-1,
	title = {Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model},
	url = {http://papers.nips.cc/paper/3828-explaining-human-multiple-object-tracking-as-resource-constrained-approximate-inference-in-a-dynamic-probabilistic-model.pdf},
	urldate = {2019-07-24},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 22},
	publisher = {Curran Associates, Inc.},
	author = {Vul, Ed and Alvarez, George and Tenenbaum, Joshua B. and Black, Michael J.},
	editor = {Bengio, Y. and Schuurmans, D. and Lafferty, J. D. and Williams, C. K. I. and Culotta, A.},
	year = {2009},
	pages = {1955--1963},
}

@article{scholl_tracking_1999,
	title = {Tracking {Multiple} {Items} {Through} {Occlusion}: {Clues} to {Visual} {Objecthood}},
	volume = {38},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0010028598906983},
	doi = {10.1006/cogp.1998.0698},
	abstract = {In three experiments, subjects attempted to track multiple items as they moved independently and unpredictably about a display. Performance was not impaired when the items were briefly (but completely) occluded at various times during their motion, suggesting that occlusion is taken into account when computing enduring perceptual objecthood. Unimpaired performance required the presence of accretion and deletion cues along fixed contours at the occluding boundaries. Performance was impaired when items were present on the visual field at the same times and to the same degrees as in the occlusion conditions, but disappeared and reappeared in ways which did not implicate the presence of occluding surfaces (e.g., by imploding and exploding into and out of existence instead of accreting and deleting along a fixed contour). Unimpaired performance did not require visible occluders (i.e., Michotte's tunnel effect) or globally consistent occluder positions. We discuss implications of these results for theories of objecthood in visual attention. © 1999 Academic Press.},
	number = {2},
	journal = {Cognitive Psychology},
	author = {Scholl, Brian J. and Pylyshyn, Zenon W.},
	year = {1999},
	pmid = {10090804},
	note = {ISBN: 0010-0285 (Print)},
	pages = {259--290},
}

@article{scholl_objects_2001,
	series = {Objects and {Attention}},
	title = {Objects and attention: the state of the art},
	volume = {80},
	issn = {0010-0277},
	shorttitle = {Objects and attention},
	url = {https://www.sciencedirect.com/science/article/pii/S0010027700001529},
	doi = {10.1016/S0010-0277(00)00152-9},
	abstract = {What are the units of attention? In addition to standard models holding that attention can select spatial regions and visual features, recent work suggests that in some cases attention can directly select discrete objects. This paper reviews the state of the art with regard to such ‘object-based’ attention, and explores how objects of attention relate to locations, reference frames, perceptual groups, surfaces, parts, and features. Also discussed are the dynamic aspects of objecthood, including the question of how attended objects are individuated in time, and the possibility of attending to simple dynamic motions and events. The final sections of this review generalize these issues beyond vision science, to other modalities and fields such as auditory objects of attention and the infant's ‘object concept’.},
	language = {en},
	number = {1},
	urldate = {2023-01-18},
	journal = {Cognition},
	author = {Scholl, Brian J},
	month = jun,
	year = {2001},
	keywords = {Attention, Objects, State of the art},
	pages = {1--46},
}

@article{yantis_multielement_1992,
	title = {Multielement visual tracking: {Attention} and perceptual organization},
	volume = {24},
	issn = {0010-0285},
	shorttitle = {Multielement visual tracking},
	url = {http://www.sciencedirect.com/science/article/pii/001002859290010Y},
	doi = {10.1016/0010-0285(92)90010-Y},
	abstract = {Two types of theories have been advanced to account for how attention is allocated in performing goal-directed visual tasks. According to location-based theories, visual attention is allocated to spatial locations in the image; according to object-based theories, attention is allocated to perceptual objects. Evidence for the latter view comes from experiments demonstrating the importance of perceptual grouping in selective-attention tasks. This article provides further evidence concerning the importance of perceptual organization in attending to objects. In seven experiments, observers tracked multiple randomly moving visual elements under a variety of conditions. Ten elements moved continuously about the display for several seconds; one to five of them were designated as targets before movement initiation. At the end of movement, one element was highlighted, and subjects indicated whether or not it was a target. The ease with which the elements in the target set could be perceptually grouped was systematically manipulated. In Experiments 1–3, factors that influenced the initial formation of a perceptual group were manipulated; this affected performance, but only early in practice. In Experiments 4–7, factors that influenced the maintenance of a perceptual group during motion were manipulated; this affected performance throughout practice. The results suggest that observers spontaneously grouped the target elements and directed attention toward this coherent but nonrigid virtual object. This supports object-based theories of attention and demonstrates that perceptual grouping, which is usually conceived of as a purely stimulus-driven process, can also be governed by goal-directed mechanisms.},
	number = {3},
	urldate = {2019-07-23},
	journal = {Cognitive Psychology},
	author = {Yantis, Steven},
	month = jul,
	year = {1992},
	pages = {295--340},
}

@book{sarkka_bayesian_2013,
	address = {Cambridge},
	title = {Bayesian {Filtering} and {Smoothing}},
	isbn = {978-1-139-34420-3},
	url = {http://ebooks.cambridge.org/ref/id/CBO9781139344203},
	language = {en},
	urldate = {2020-09-08},
	publisher = {Cambridge University Press},
	author = {Sarkka, Simo},
	year = {2013},
	doi = {10.1017/CBO9781139344203},
}

@article{rust_praise_2005,
	title = {In praise of artifice},
	volume = {8},
	copyright = {2005 Nature Publishing Group},
	issn = {1546-1726},
	url = {https://www.nature.com/articles/nn1606},
	doi = {10.1038/nn1606},
	abstract = {The visual system evolved to process natural images, and the goal of visual neuroscience is to understand the computations it uses to do this. Indeed the goal of any theory of visual function is a model that will predict responses to any stimulus, including natural scenes. It has, however, recently become common to take this fundamental principle one step further: trying to use photographic or cinematographic representations of natural scenes (natural stimuli) as primary probes to explore visual computations. This approach is both challenging and controversial, and we argue that this use of natural images is so fraught with difficulty that it is not useful. Traditional methods for exploring visual computations that use artificial stimuli with carefully selected properties have been and continue to be the most effective tools for visual neuroscience. The proper use of natural stimuli is to test models based on responses to these synthetic stimuli, not to replace them.},
	language = {en},
	number = {12},
	urldate = {2020-06-10},
	journal = {Nature Neuroscience},
	author = {Rust, Nicole C. and Movshon, J. Anthony},
	month = dec,
	year = {2005},
	note = {Number: 12
Publisher: Nature Publishing Group},
	pages = {1647--1650},
}

@incollection{ren_faster_2015,
	title = {Faster {R}-{CNN}: {Towards} {Real}-{Time} {Object} {Detection} with {Region} {Proposal} {Networks}},
	shorttitle = {Faster {R}-{CNN}},
	url = {http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf},
	urldate = {2019-07-30},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 28},
	publisher = {Curran Associates, Inc.},
	author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
	editor = {Cortes, C. and Lawrence, N. D. and Lee, D. D. and Sugiyama, M. and Garnett, R.},
	year = {2015},
	pages = {91--99},
}

@article{pylyshyn_tracking_1988,
	title = {Tracking multiple independent targets: {Evidence} for a parallel tracking mechanism},
	volume = {3},
	issn = {0169-1015, 1568-5683},
	shorttitle = {Tracking multiple independent targets},
	url = {https://brill.com/view/journals/sv/3/3/article-p179_3.xml},
	doi = {10.1163/156856888X00122},
	abstract = {There is considerable evidence that visual attention is concentrated at a single locus in the visual field, and that this locus can be moved independent of eye movements. Two studies are reported which suggest that, while certain aspects of attention require that locations{\textbackslash}be scanned serially, at least one operation may be carried out in parallel across several independent loci in the visual field. That is the operation of indexing features and tracking their identity. The studies show that: (a) subjects are able to track a subset of up to 5 objects in a field of 10 'identical randomly-moving objects in order to distinguish a change in a target from a change in a distractor; and (b) when the speed and distance parameters of the display are designed so that, on the basis of some very conservative assumptions about the speed of attention movement and encoding times, the predicted performance of a serial scanning and updating algorithm would not exceed about 40\% accuracy, subjects still manage to do the task with 87\% accuracy. These findings are discussed in relation to an earlier, and independently motivated model of featurebinding-called the FINST model-which posits a primitive identity maintenance mechanism that indexes and tracks a limited number of visual objects in parallel. These indexes are hypothesized to serve the function of binding visual features prior to subsequent pattern recognition.},
	language = {en},
	number = {3},
	urldate = {2019-07-23},
	journal = {Spatial Vision},
	author = {Pylyshyn, Zenon W. and Storm, Ron W.},
	year = {1988},
	pages = {179--197},
}

@inproceedings{peters_flying_2022,
	address = {San Francisco},
	title = {Flying {Objects}: {Challenging} humans and machines in dynamic object vision},
	shorttitle = {Flying {Objects}},
	url = {https://2022.ccneuro.org/view_paper.php?PaperNum=1301},
	doi = {10.32470/CCN.2022.1301-0},
	urldate = {2022-09-23},
	booktitle = {2022 {Conference} on {Cognitive} {Computational} {Neuroscience}},
	publisher = {Cognitive Computational Neuroscience},
	author = {Peters, Benjamin and Retchin, Matthew and Kriegeskorte, Nikolaus},
	year = {2022},
}

@article{papenmeier_tracking_2014,
	title = {Tracking by location and features: {Object} correspondence across spatiotemporal discontinuities during multiple object tracking},
	volume = {40},
	issn = {1939-1277(Electronic),0096-1523(Print)},
	shorttitle = {Tracking by location and features},
	url = {https://psycnet.apa.org/doi/10.1037/a0033117},
	doi = {10.1037/a0033117},
	abstract = {We examined whether surface feature information is utilized to track the locations of multiple objects. In particular, we tested whether surface features and spatiotemporal information are weighted according to their availability and reliability. Accordingly, we hypothesized that surface features should affect location tracking across spatiotemporal discontinuities. Three kinds of spatiotemporal discontinuities were implemented across five experiments: abrupt scene rotations, abrupt zooms, and a reduced presentation frame rate. Objects were briefly colored across the spatiotemporal discontinuity. Distinct coloring that matched spatiotemporal information across the discontinuity improved tracking performance as compared with homogeneous coloring. Swapping distinct colors across the discontinuity impaired performance. Correspondence by color was further demonstrated by more mis-selected distractors appearing in a former target color than distractors appearing in a former distractor color in the swap condition. This was true even when color never supported tracking and when participants were instructed to ignore color. Furthermore, effects of object color on tracking occurred with unreliable spatiotemporal information but not with reliable spatiotemporal information. Our results demonstrate that surface feature information can be utilized to track the locations of multiple objects. This is in contrast to theories stating that objects are tracked based on spatiotemporal information only. We introduce a flexible-weighting tracking account stating that spatiotemporal information and surface features are both utilized by the location tracking mechanism. The two sources of information are weighted according to their availability and reliability. Surface feature effects on tracking are particularly likely when distinct surface feature information is available and spatiotemporal information is unreliable. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
	number = {1},
	journal = {Journal of Experimental Psychology: Human Perception and Performance},
	author = {Papenmeier, Frank and Meyerhoff, Hauke S. and Jahn, Georg and Huff, Markus},
	year = {2014},
	keywords = {Color, Form and Shape Perception, Tracking, Visual Attention},
	pages = {159--171},
}

@inproceedings{pang_quasi-dense_2021,
	title = {Quasi-dense similarity learning for multiple object tracking},
	booktitle = {Proceedings of the {IEEE}/{CVF} conference on computer vision and pattern recognition},
	author = {Pang, Jiangmiao and Qiu, Linlu and Li, Xia and Chen, Haofeng and Li, Qi and Darrell, Trevor and Yu, Fisher},
	year = {2021},
	pages = {164--173},
}

@article{oksama_dynamic_2008,
	title = {Dynamic binding of identity and location information: {A} serial model of multiple identity tracking},
	volume = {56},
	issn = {0010-0285},
	shorttitle = {Dynamic binding of identity and location information},
	url = {http://www.sciencedirect.com/science/article/pii/S0010028507000126},
	doi = {10.1016/j.cogpsych.2007.03.001},
	abstract = {Tracking of multiple moving objects is commonly assumed to be carried out by a fixed-capacity parallel mechanism. The present study proposes a serial model (MOMIT) to explain performance accuracy in the maintenance of multiple moving objects with distinct identities. A serial refresh mechanism is postulated, which makes recourse to continuous attention switching, a capacity-limited episodic buffer for identity-location bindings, indexed location information stored in the visuospatial short-term memory, and an active role of long-term memory. As identity-location bindings are refreshed serially, a location error is inherent for all other targets except the focally attended one. The magnitude of this location error is a key factor in predicting tracking accuracy. MOMIT’s predictions were supported by the data of five experiments: performance accuracy decreased as a function of target set-size, speed, and familiarity. A mathematical version of MOMIT fitted nicely to the observed data with plausible parameter estimates for the binding capacity and refresh time.},
	number = {4},
	urldate = {2019-08-04},
	journal = {Cognitive Psychology},
	author = {Oksama, Lauri and Hyönä, Jukka},
	month = jun,
	year = {2008},
	keywords = {Change detection, Dynamic visual scenes, Episodic buffer, Identity-location binding, Long-term memory, Multiple identity tracking, Multiple object tracking, Visuospatial short-term memory},
	pages = {237--283},
}

@article{oksama_is_2004,
	title = {Is multiple object tracking carried out automatically by an early vision mechanism independent of higher‐order cognition? {An} individual difference approach},
	volume = {11},
	issn = {1350-6285},
	shorttitle = {Is multiple object tracking carried out automatically by an early vision mechanism independent of higher‐order cognition?},
	url = {https://doi.org/10.1080/13506280344000473},
	doi = {10.1080/13506280344000473},
	abstract = {Existing theories of multiple object tracking (MOT) offer different predictions concerning the role of higher level cognitive processes, individual differences, effortful attention and parallel processing in MOT. Pylyshyn's model (1989) argues for an automatic parallel processing mechanism separate from other cognition, whereas alternative models (e.g., Kahneman \& Treisman, 1984 or spotlight models) are based on higher level cognition such as spatial short‐term memory and/or effortful attention switching. These predictions were examined in Experiment 1 where identical objects and in Experiment 2 where visually and semantically distinct objects were tracked. Both experiments demonstrated a substantial individual variation in the estimated tracking capacity. Tasks measuring visuospatial short‐term memory and attention switching proved to be significant predictors of MOT. In addition, tracking performance deteriorated as a function of tracking time and set size. Our results are in contrast to Pylyshyn's model. A mechanism with both parallel and serial processing and temporary spatial memory is outlined to accommodate the observed pattern of results.},
	number = {5},
	urldate = {2023-01-18},
	journal = {Visual Cognition},
	author = {Oksama, Lauri and Hyönä, Jukka},
	month = jul,
	year = {2004},
	note = {Publisher: Routledge
\_eprint: https://doi.org/10.1080/13506280344000473},
	pages = {631--671},
}

@incollection{ferrari_trackingnet_2018,
	address = {Cham},
	title = {{TrackingNet}: {A} {Large}-{Scale} {Dataset} and {Benchmark} for {Object} {Tracking} in the {Wild}},
	volume = {11205},
	isbn = {978-3-030-01245-8 978-3-030-01246-5},
	shorttitle = {{TrackingNet}},
	url = {http://link.springer.com/10.1007/978-3-030-01246-5_19},
	abstract = {Despite the numerous developments in object tracking, further improvement of current tracking algorithms is limited by small and mostly saturated datasets. As a matter of fact, data-hungry trackers based on deep-learning currently rely on object detection datasets due to the scarcity of dedicated large-scale tracking datasets. In this work, we present TrackingNet, the ﬁrst large-scale dataset and benchmark for object tracking in the wild. We provide more than 30K videos with more than 14 million dense bounding box annotations. Our dataset covers a wide selection of object classes in broad and diverse context. By releasing such a large-scale dataset, we expect deep trackers to further improve and generalize. In addition, we introduce a new benchmark composed of 500 novel videos, modeled with a distribution similar to our training dataset. By sequestering the annotation of the test set and providing an online evaluation server, we provide a fair benchmark for future development of object trackers. Deep trackers ﬁne-tuned on a fraction of our dataset improve their performance by up to 1.6\% on OTB100 and up to 1.7\% on TrackingNet Test. We provide an extensive benchmark on TrackingNet by evaluating more than 20 trackers. Our results suggest that object tracking in the wild is far from being solved.},
	language = {en},
	urldate = {2022-09-25},
	booktitle = {Computer {Vision} – {ECCV} 2018},
	publisher = {Springer International Publishing},
	author = {Müller, Matthias and Bibi, Adel and Giancola, Silvio and Alsubaihi, Salman and Ghanem, Bernard},
	editor = {Ferrari, Vittorio and Hebert, Martial and Sminchisescu, Cristian and Weiss, Yair},
	year = {2018},
	doi = {10.1007/978-3-030-01246-5_19},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {310--327},
}

@article{milan_mot16_2016,
	title = {{MOT16}: {A} {Benchmark} for {Multi}-{Object} {Tracking}},
	shorttitle = {{MOT16}},
	url = {http://arxiv.org/abs/1603.00831},
	abstract = {Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for reseach. Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods. The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. This paper accompanies a new release of the MOTChallenge benchmark. Unlike the initial release, all videos of MOT16 have been carefully annotated following a consistent protocol. Moreover, it not only offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest.},
	urldate = {2021-05-05},
	journal = {arXiv:1603.00831 [cs]},
	author = {Milan, Anton and Leal-Taixe, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
	month = may,
	year = {2016},
	note = {arXiv: 1603.00831},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{malik_challenge_2021,
	title = {The {Challenge} of {Appearance}-{Free} {Object} {Tracking} with {Feedforward} {Neural} {Networks}},
	url = {http://arxiv.org/abs/2110.02772},
	abstract = {Nearly all models for object tracking with artificial neural networks depend on appearance features extracted from a "backbone" architecture, designed for object recognition. Indeed, significant progress on object tracking has been spurred by introducing backbones that are better able to discriminate objects by their appearance. However, extensive neurophysiology and psychophysics evidence suggests that biological visual systems track objects using both appearance and motion features. Here, we introduce \${\textbackslash}textit\{PathTracker\}\$, a visual challenge inspired by cognitive psychology, which tests the ability of observers to learn to track objects solely by their motion. We find that standard 3D-convolutional deep network models struggle to solve this task when clutter is introduced into the generated scenes, or when objects travel long distances. This challenge reveals that tracing the path of object motion is a blind spot of feedforward neural networks. We expect that strategies for appearance-free object tracking from biological vision can inspire solutions these failures of deep neural networks.},
	urldate = {2021-12-20},
	journal = {arXiv:2110.02772 [cs]},
	author = {Malik, Girik and Linsley, Drew and Serre, Thomas and Mingolla, Ennio},
	month = sep,
	year = {2021},
	note = {arXiv: 2110.02772},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
}

@article{merkel_how_2019,
	title = {How to {Perceive} {Object} {Permanence} in {Our} {Visual} {Environment}: {The} {Multiple} {Object} {Tracking} {Paradigm}},
	shorttitle = {How to {Perceive} {Object} {Permanence} in {Our} {Visual} {Environment}},
	url = {https://link.springer.com/protocol/10.1007/7657_2019_28},
	doi = {10.1007/7657_2019_28},
	abstract = {The ability to simultaneously maintain multiple representations through motion is an essential feature of the visual system. The multiple object tracking paradigm (MOT) has been devised in order to...},
	language = {en},
	urldate = {2019-11-01},
	author = {Merkel, Christian and Hopf, Jens-Max and Schoenfeld, Mircea Ariel},
	year = {2019},
	pages = {1--20},
}

@article{ma_neural_2020,
	title = {A neural network walks into a lab: towards using deep nets as models for human behavior},
	copyright = {All rights reserved},
	shorttitle = {A neural network walks into a lab},
	url = {http://arxiv.org/abs/2005.02181},
	abstract = {What might sound like the beginning of a joke has become an attractive prospect for many cognitive scientists: the use of deep neural network models (DNNs) as models of human behavior in perceptual and cognitive tasks. Although DNNs have taken over machine learning, attempts to use them as models of human behavior are still in the early stages. Can they become a versatile model class in the cognitive scientist's toolbox? We first argue why DNNs have the potential to be interesting models of human behavior. We then discuss how that potential can be more fully realized. On the one hand, we argue that the cycle of training, testing, and revising DNNs needs to be revisited through the lens of the cognitive scientist's goals. Specifically, we argue that methods for assessing the goodness of fit between DNN models and human behavior have to date been impoverished. On the other hand, cognitive science might have to start using more complex tasks (including richer stimulus spaces), but doing so might be beneficial for DNN-independent reasons as well. Finally, we highlight avenues where traditional cognitive process models and DNNs may show productive synergy.},
	urldate = {2020-05-23},
	journal = {arXiv:2005.02181 [cs, q-bio]},
	author = {Ma, Wei Ji and Peters, Benjamin},
	month = may,
	year = {2020},
	note = {arXiv: 2005.02181},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition},
}

@article{ma_no_2009,
	title = {No capacity limit in attentional tracking: {Evidence} for probabilistic inference under a resource constraint},
	volume = {9},
	issn = {1534-7362},
	shorttitle = {No capacity limit in attentional tracking},
	url = {https://jov.arvojournals.org/article.aspx?articleid=2203974},
	doi = {10.1167/9.11.3},
	language = {en},
	number = {11},
	urldate = {2019-11-25},
	journal = {Journal of Vision},
	author = {Ma, Wei Ji and Huang, Wei},
	month = oct,
	year = {2009},
	pages = {3--3},
}

@inproceedings{linsley_tracking_2021,
	title = {Tracking {Without} {Re}-recognition in {Humans} and {Machines}},
	volume = {34},
	url = {https://proceedings.neurips.cc/paper/2021/file/a2557a7b2e94197ff767970b67041697-Paper.pdf},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Linsley, Drew and Malik, Girik and Kim, Junkyung and Govindarajan, Lakshmi Narasimhan and Mingolla, Ennio and Serre, Thomas},
	editor = {Ranzato, M. and Beygelzimer, A. and Dauphin, Y. and Liang, P. S. and Vaughan, J. Wortman},
	year = {2021},
	pages = {19473--19486},
}

@misc{lin_microsoft_2015,
	title = {Microsoft {COCO}: {Common} {Objects} in {Context}},
	shorttitle = {Microsoft {COCO}},
	url = {http://arxiv.org/abs/1405.0312},
	doi = {10.48550/arXiv.1405.0312},
	abstract = {We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.},
	publisher = {arXiv},
	author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Bourdev, Lubomir and Girshick, Ross and Hays, James and Perona, Pietro and Ramanan, Deva and Zitnick, C. Lawrence and Dollár, Piotr},
	month = feb,
	year = {2015},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{li_model_2019,
	title = {Model of {Multiple} {Identity} {Tracking} ({MOMIT}) 2.0: {Resolving} the serial vs. parallel controversy in tracking},
	volume = {182},
	issn = {0010-0277},
	shorttitle = {Model of {Multiple} {Identity} {Tracking} ({MOMIT}) 2.0},
	url = {http://www.sciencedirect.com/science/article/pii/S0010027718302749},
	doi = {10.1016/j.cognition.2018.10.016},
	abstract = {The present study investigated whether during tracking of multiple moving objects with distinct identities only one identity is tracked at each moment (serial tracking) or whether multiple identities can be tracked simultaneously (parallel tracking). By adopting the gaze-contingent display change technique, we manipulated in real time the presence/absence of object identities during tracking. The data on performance accuracy revealed a serial tracking pattern for facial images and a parallel pattern for color discs: when tracking faces, the presence/absence of only the currently foveated identity impacted the performance, whereas when tracking colors, the presence of multiple identities across the visual field led to improved tracking performance. This pattern is consistent with the identifiability of the different types of objects in the visual field. The eye movements during MIT showed a bias towards visiting and dwelling on individual targets when facial identities were present and towards visiting the blank areas between targets when color identities were present. Nevertheless, the eye visits were predominately on individual targets regardless of the type of objects and the presence of object identities. The eye visits to targets were beneficial for target tracking, particularly in face tracking. We propose the Model of Multiple Identity Tracking (MOMIT) 2.0 which accounts for the results and reconcile the serial vs. parallel controversy. The model suggests that observers cooperatively use attention, eye movements, perception, and working memory for dynamic tracking. Tracking appears more serial when high-resolution information needs to be sampled and maintained for discriminating the targets, whereas it appears more parallel when low-resolution information is sufficient.},
	language = {en},
	urldate = {2019-11-25},
	journal = {Cognition},
	author = {Li, Jie and Oksama, Lauri and Hyönä, Jukka},
	month = jan,
	year = {2019},
	keywords = {Eye movements, Gaze-contingent display change, Multiple-identity tracking, Visual attention, Working memory},
	pages = {260--274},
}

@inproceedings{kim_multiple_2015,
	title = {Multiple {Hypothesis} {Tracking} {Revisited}},
	url = {https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Kim_Multiple_Hypothesis_Tracking_ICCV_2015_paper.html},
	urldate = {2022-09-24},
	author = {Kim, Chanho and Li, Fuxin and Ciptadi, Arridhana and Rehg, James M.},
	year = {2015},
	pages = {4696--4704},
}

@misc{leal-taixe_motchallenge_2015,
	title = {{MOTChallenge} 2015: {Towards} a {Benchmark} for {Multi}-{Target} {Tracking}},
	shorttitle = {{MOTChallenge} 2015},
	url = {http://arxiv.org/abs/1504.01942},
	doi = {10.48550/arXiv.1504.01942},
	abstract = {In the recent past, the computer vision community has developed centralized benchmarks for the performance evaluation of a variety of tasks, including generic object and pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they have proved to be extremely helpful to advance the state of the art in the respective area. Interestingly, there has been rather limited work on the standardization of quantitative benchmarks for multiple target tracking. One of the few exceptions is the well-known PETS dataset, targeted primarily at surveillance applications. Despite being widely used, it is often applied inconsistently, for example involving using different subsets of the available data, different ways of training the models, or differing evaluation scripts. This paper describes our work toward a novel multiple object tracking benchmark aimed to address such issues. We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system. With MOTChallenge we aim to pave the way toward a unified evaluation framework for a more meaningful quantification of multi-target tracking.},
	urldate = {2022-09-23},
	publisher = {arXiv},
	author = {Leal-Taixé, Laura and Milan, Anton and Reid, Ian and Roth, Stefan and Schindler, Konrad},
	month = apr,
	year = {2015},
	note = {arXiv:1504.01942 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{kahneman_reviewing_1992,
	title = {The reviewing of object-files: {Object} specific integration of information.},
	volume = {24},
	number = {2},
	journal = {Cognitive Psychology},
	author = {Kahneman, D and Treisman, Anne and Gibbs, B J},
	year = {1992},
	note = {ISBN: 0010-0285},
	pages = {174--219},
}

@article{greff_binding_2020,
	title = {On the {Binding} {Problem} in {Artificial} {Neural} {Networks}},
	url = {http://arxiv.org/abs/2012.05208},
	doi = {doi.org/10.48550/arXiv.2012.05208},
	abstract = {Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information that is distributed throughout the network. This binding problem affects their capacity to acquire a compositional understanding of the world in terms of symbol-like entities (like objects), which is crucial for generalizing in predictable and systematic ways. To address this issue, we propose a unifying framework that revolves around forming meaningful entities from unstructured sensory inputs (segregation), maintaining this separation of information at a representational level (representation), and using these entities to construct new inferences, predictions, and behaviors (composition). Our analysis draws inspiration from a wealth of research in neuroscience and cognitive psychology, and surveys relevant mechanisms from the machine learning literature, to help identify a combination of inductive biases that allow symbolic information processing to emerge naturally in neural networks. We believe that a compositional approach to AI, in terms of grounded symbol-like representations, is of fundamental importance for realizing human-level generalization, and we hope that this paper may contribute towards that goal as a reference and inspiration.},
	journal = {arXiv:2012.05208 [cs]},
	author = {Greff, Klaus and van Steenkiste, Sjoerd and Schmidhuber, Jürgen},
	month = dec,
	year = {2020},
	note = {arXiv: 2012.05208},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, I.2.6},
}

@article{gray_game-xp_2017,
	title = {Game-{XP}: {Action} {Games} as {Experimental} {Paradigms} for {Cognitive} {Science}},
	volume = {9},
	issn = {1756-8765},
	shorttitle = {Game-{XP}},
	doi = {10.1111/tops.12260},
	abstract = {Why games? How could anyone consider action games an experimental paradigm for Cognitive Science? In 1973, as one of three strategies he proposed for advancing Cognitive Science, Allen Newell exhorted us to "accept a single complex task and do all of it." More specifically, he told us that rather than taking an "experimental psychology as usual approach," we should "focus on a series of experimental and theoretical studies around a single complex task" so as to demonstrate that our theories of human cognition were powerful enough to explain "a genuine slab of human behavior" with the studies fitting into a detailed theoretical picture. Action games represent the type of experimental paradigm that Newell was advocating and the current state of programming expertise and laboratory equipment, along with the emergence of Big Data and naturally occurring datasets, provide the technologies and data needed to realize his vision. Action games enable us to escape from our field's regrettable focus on novice performance to develop theories that account for the full range of expertise through a twin focus on expertise sampling (across individuals) and longitudinal studies (within individuals) of simple and complex tasks.},
	language = {eng},
	number = {2},
	journal = {Topics in Cognitive Science},
	author = {Gray, Wayne D.},
	month = apr,
	year = {2017},
	pmid = {28296290},
	keywords = {Action games, Chess, Cognition, Cognitive Science, Cognitive skill acquisition, Cohort analysis, Computer games, Expertise sampling, Extreme expertise, Halo, Humans, Longitudinal studies, Skilled performance, Space Fortress, StarCraft, Tetris, Verbal protocol analysis, Video Games, Video games},
	pages = {289--307},
}

@article{intriligator_spatial_2001,
	title = {The spatial resolution of visual attention},
	volume = {43},
	url = {http://www.sciencedirect.com/science/article/pii/S0010028501907558},
	doi = {10.1006/cogp.2001.0755},
	abstract = {Two tasks were used to evaluate the grain of visual attention, the minimum spacing at which attention can select individual items. First, observers performed a tracking task at many viewing distances. When the display subtended less than 1° in size, tracking was no longer possible even though observers could resolve the items and their motions: The items were visible but could not be individuated one from the other. The limiting size for selection was roughly the same whether tracking one or three targets, suggesting that the resolution limit acts independently of the capacity limit of attention. Second, the closest spacing that still allowed individuation of single items in dense, static displays was examined. This critical spacing was about 50\% coarser in the radial direction compared to the tangential direction and was coarser in the upper as opposed to the lower visual field. The results suggest that no more than about 60 items can be arrayed in the central 30° of the visual field while still allowing attentional access to each individually. Our data show that selection has a coarse grain, much coarser than visual resolution. These measures of the resolution of attention are based solely on the selection of location and are not confounded with preattentive feature interactions that may contribute to measures from flanker and crowding tasks. The results suggest that the parietal area is the most likely locus of this selection mechanism and that it acts by pointing to the spatial coordinates (or cortical coordinates) of items of interest rather than by holding a representation of the items themselves.},
	number = {3},
	journal = {Cognitive Psychology},
	author = {Intriligator, James and Cavanagh, Patrick},
	year = {2001},
	pages = {171--216},
}

@inproceedings{geiger_are_2012,
	title = {Are we ready for {Autonomous} {Driving}? {The} {KITTI} {Vision} {Benchmark} {Suite}},
	url = {https://ieeexplore.ieee.org/document/6248074},
	doi = {10.1109/CVPR.2012.6248074},
	booktitle = {Conference on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {Geiger, Andreas and Lenz, Philip and Urtasun, Raquel},
	year = {2012},
}

@misc{dendorfer_mot20_2020,
	title = {{MOT20}: {A} benchmark for multi object tracking in crowded scenes},
	shorttitle = {{MOT20}},
	url = {http://arxiv.org/abs/2003.09003},
	doi = {10.48550/arXiv.2003.09003},
	abstract = {Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16, and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our MOT20benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark was presented first at the 4thBMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and gives to chance to evaluate state-of-the-art methods for multiple object tracking when handling extremely crowded scenarios.},
	publisher = {arXiv},
	author = {Dendorfer, Patrick and Rezatofighi, Hamid and Milan, Anton and Shi, Javen and Cremers, Daniel and Reid, Ian and Roth, Stefan and Schindler, Konrad and Leal-Taixé, Laura},
	month = mar,
	year = {2020},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{fabbri_motsynth_2021,
	title = {{MOTSynth}: {How} {Can} {Synthetic} {Data} {Help} {Pedestrian} {Detection} and {Tracking}?},
	shorttitle = {{MOTSynth}},
	url = {http://arxiv.org/abs/2108.09518},
	doi = {10.48550/arXiv.2108.09518},
	abstract = {Deep learning-based methods for video pedestrian detection and tracking require large volumes of training data to achieve good performance. However, data acquisition in crowded public environments raises data privacy concerns -- we are not allowed to simply record and store data without the explicit consent of all participants. Furthermore, the annotation of such data for computer vision applications usually requires a substantial amount of manual effort, especially in the video domain. Labeling instances of pedestrians in highly crowded scenarios can be challenging even for human annotators and may introduce errors in the training data. In this paper, we study how we can advance different aspects of multi-person tracking using solely synthetic data. To this end, we generate MOTSynth, a large, highly diverse synthetic dataset for object detection and tracking using a rendering game engine. Our experiments show that MOTSynth can be used as a replacement for real data on tasks such as pedestrian detection, re-identification, segmentation, and tracking.},
	urldate = {2022-09-23},
	publisher = {arXiv},
	author = {Fabbri, Matteo and Braso, Guillem and Maugeri, Gianluca and Cetintas, Orcun and Gasparini, Riccardo and Osep, Aljosa and Calderara, Simone and Leal-Taixe, Laura and Cucchiara, Rita},
	month = aug,
	year = {2021},
	note = {arXiv:2108.09518 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{dave_tao_2020,
	title = {{TAO}: {A} {Large}-{Scale} {Benchmark} for {Tracking} {Any} {Object}},
	shorttitle = {{TAO}},
	url = {http://arxiv.org/abs/2005.10356},
	doi = {10.48550/arXiv.2005.10356},
	abstract = {For many years, multi-object tracking benchmarks have focused on a handful of categories. Motivated primarily by surveillance and self-driving applications, these datasets provide tracks for people, vehicles, and animals, ignoring the vast majority of objects in the world. By contrast, in the related field of object detection, the introduction of large-scale, diverse datasets (e.g., COCO) have fostered significant progress in developing highly robust solutions. To bridge this gap, we introduce a similarly diverse dataset for Tracking Any Object (TAO). It consists of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average. Importantly, we adopt a bottom-up approach for discovering a large vocabulary of 833 categories, an order of magnitude more than prior tracking benchmarks. To this end, we ask annotators to label objects that move at any point in the video, and give names to them post factum. Our vocabulary is both significantly larger and qualitatively different from existing tracking datasets. To ensure scalability of annotation, we employ a federated approach that focuses manual effort on labeling tracks for those relevant objects in a video (e.g., those that move). We perform an extensive evaluation of state-of-the-art trackers and make a number of important discoveries regarding large-vocabulary tracking in an open-world. In particular, we show that existing single- and multi-object trackers struggle when applied to this scenario in the wild, and that detection-based, multi-object trackers are in fact competitive with user-initialized ones. We hope that our dataset and analysis will boost further progress in the tracking community.},
	urldate = {2022-09-23},
	publisher = {arXiv},
	author = {Dave, Achal and Khurana, Tarasha and Tokmakov, Pavel and Schmid, Cordelia and Ramanan, Deva},
	month = may,
	year = {2020},
	note = {arXiv:2005.10356 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{contributors_mmtracking_2020,
	title = {{MMTracking}: {OpenMMLab} video perception toolbox and benchmark},
	url = {https://github.com/open-mmlab/mmtracking},
	author = {Contributors, MMTracking},
	year = {2020},
}

@inproceedings{bergmann_tracking_2019,
	title = {Tracking without bells and whistles},
	booktitle = {Proceedings of the {IEEE} international conference on computer vision},
	author = {Bergmann, Philipp and Meinhardt, Tim and Leal-Taixe, Laura},
	year = {2019},
	pages = {941--951},
}

@article{ciaparrone_deep_2020,
	title = {Deep {Learning} in {Video} {Multi}-{Object} {Tracking}: {A} {Survey}},
	volume = {381},
	issn = {09252312},
	shorttitle = {Deep {Learning} in {Video} {Multi}-{Object} {Tracking}},
	url = {http://arxiv.org/abs/1907.12740},
	doi = {10.1016/j.neucom.2019.11.023},
	abstract = {The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learning, the algorithms that provide a solution to this problem have beneﬁted from the representational power of deep models. This paper provides a comprehensive survey on works that employ Deep Learning models to solve the task of MOT on single-camera videos. Four main steps in MOT algorithms are identiﬁed, and an in-depth review of how Deep Learning was employed in each one of these stages is presented. A complete experimental comparison of the presented works on the three MOTChallenge datasets is also provided, identifying a number of similarities among the top-performing methods and presenting some possible future research directions.},
	language = {en},
	urldate = {2020-02-18},
	journal = {Neurocomputing},
	author = {Ciaparrone, Gioele and Sánchez, Francisco Luque and Tabik, Siham and Troiano, Luigi and Tagliaferri, Roberto and Herrera, Francisco},
	month = mar,
	year = {2020},
	note = {arXiv: 1907.12740},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
	pages = {61--88},
}

@article{cavanagh_tracking_2005,
	title = {Tracking multiple targets with multifocal attention},
	volume = {9},
	issn = {1364-6613},
	url = {https://www.sciencedirect.com/science/article/pii/S136466130500149X},
	doi = {10.1016/j.tics.2005.05.009},
	abstract = {Attention allows us to monitor objects or regions of visual space and select information from them for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as video games, navigating busy intersections, or watching over children at a swimming pool, require attention to multiple regions of interest. Laboratory tracking tasks have indeed demonstrated the ability to track four or more targets simultaneously. Although the mechanisms by which attention maintains contact with several targets are not yet established, recent studies have identified several characteristics of the tracking process, including properties defining a ‘trackable’ target, the maximum number of targets that can be tracked, and the hemifield independence of the tracking process. This research also has implications for computer vision, where there is a growing demand for multiple-object tracking.},
	language = {en},
	number = {7},
	urldate = {2023-01-18},
	journal = {Trends in Cognitive Sciences},
	author = {Cavanagh, Patrick and Alvarez, George A.},
	month = jul,
	year = {2005},
	pages = {349--354},
}

@article{cao_observation-centric_2022,
	title = {Observation-{Centric} {SORT}: {Rethinking} {SORT} for {Robust} {Multi}-{Object} {Tracking}},
	url = {https://arxiv.org/abs/2203.14360},
	doi = {10.48550/arXiv.2203.14360},
	journal = {arXiv preprint arXiv:2203.14360},
	author = {Cao, Jinkun and Weng, Xinshuo and Khirodkar, Rawal and Pang, Jiangmiao and Kitani, Kris},
	year = {2022},
}

@misc{voigtlaender_mots_2019,
	title = {{MOTS}: {Multi}-{Object} {Tracking} and {Segmentation}},
	shorttitle = {{MOTS}},
	url = {http://arxiv.org/abs/1902.03604},
	doi = {10.48550/arXiv.1902.03604},
	abstract = {This paper extends the popular task of multi-object tracking to multi-object tracking and segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two existing tracking datasets using a semi-automatic annotation procedure. Our new annotations comprise 65,213 pixel masks for 977 distinct objects (cars and pedestrians) in 10,870 video frames. For evaluation, we extend existing multi-object tracking metrics to this new task. Moreover, we propose a new baseline method which jointly addresses detection, tracking, and segmentation with a single convolutional network. We demonstrate the value of our datasets by achieving improvements in performance when training on MOTS annotations. We believe that our datasets, metrics and baseline will become a valuable resource towards developing multi-object tracking approaches that go beyond 2D bounding boxes. We make our annotations, code, and models available at https://www.vision.rwth-aachen.de/page/mots.},
	urldate = {2022-09-23},
	publisher = {arXiv},
	author = {Voigtlaender, Paul and Krause, Michael and Osep, Aljosa and Luiten, Jonathon and Sekar, Berin Balachandar Gnana and Geiger, Andreas and Leibe, Bastian},
	month = apr,
	year = {2019},
	note = {arXiv:1902.03604 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{thornton_interactive_2014,
	title = {Interactive {Multiple} {Object} {Tracking} ({iMOT})},
	volume = {9},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086974},
	doi = {10.1371/journal.pone.0086974},
	abstract = {We introduce a new task for exploring the relationship between action and attention. In this interactive multiple object tracking (iMOT) task, implemented as an iPad app, participants were presented with a display of multiple, visually identical disks which moved independently. The task was to prevent any collisions during a fixed duration. Participants could perturb object trajectories via the touchscreen. In Experiment 1, we used a staircase procedure to measure the ability to control moving objects. Object speed was set to 1°/s. On average participants could control 8.4 items without collision. Individual control strategies were quite variable, but did not predict overall performance. In Experiment 2, we compared iMOT with standard MOT performance using identical displays. Object speed was set to 2°/s. Participants could reliably control more objects (M = 6.6) than they could track (M = 4.0), but performance in the two tasks was positively correlated. In Experiment 3, we used a dual-task design. Compared to single-task baseline, iMOT performance decreased and MOT performance increased when the two tasks had to be completed together. Overall, these findings suggest: 1) There is a clear limit to the number of items that can be simultaneously controlled, for a given speed and display density; 2) participants can control more items than they can track; 3) task-relevant action appears not to disrupt MOT performance in the current experimental context.},
	language = {en},
	number = {2},
	urldate = {2019-11-25},
	journal = {PLOS ONE},
	author = {Thornton, Ian M. and Bülthoff, Heinrich H. and Horowitz, Todd S. and Rynning, Aksel and Lee, Seong-Whan},
	month = feb,
	year = {2014},
	keywords = {Analysis of variance, Attention, Consumer electronics, Experimental design, Games, Human performance, Reaction time, Target detection},
	pages = {e86974},
}

@techreport{streit_probabilistic_1995,
	title = {Probabilistic {Multi}-{Hypothesis} {Tracking}},
	url = {https://apps.dtic.mil/docs/citations/ADA298501},
	abstract = {In a multitarget, multimeasurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. This study is a probabilistic approach to the measurement-to-track assignment problem. Measurements are not assigned to tracks as in traditional multi-hypothesis tracking (MHT) algorithms; Instead, the probability that each measurement belongs to each track is estimated using a maximum a posteriori (MAP) method. These measurement-to-track probability estimates are intrinsic to the multitarget tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning. The PMHT algorithm is an optimal MAP multitarget tracking algorithm. (AN)},
	language = {en},
	number = {NUWC-NPT-TR-10428},
	urldate = {2019-06-20},
	institution = {NAVAL UNDERWATER SYSTEMS CENTER NEWPORT RI},
	author = {Streit, Roy L. and Luginbuhl, Tod E.},
	month = feb,
	year = {1995},
}

@inproceedings{wu_online_2013,
	title = {Online {Object} {Tracking}: {A} {Benchmark}},
	shorttitle = {Online {Object} {Tracking}},
	url = {https://www.cv-foundation.org/openaccess/content_cvpr_2013/html/Wu_Online_Object_Tracking_2013_CVPR_paper.html},
	urldate = {2022-09-25},
	author = {Wu, Yi and Lim, Jongwoo and Yang, Ming-Hsuan},
	year = {2013},
	pages = {2411--2418},
}

@inproceedings{zhang_bytetrack_2022,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {{ByteTrack}: {Multi}-object {Tracking} by {Associating} {Every} {Detection} {Box}},
	shorttitle = {{ByteTrack}},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-20047-2_1},
	doi = {10.1007/978-3-031-20047-2_1},
	abstract = {Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating almost every detection box instead of only the high score ones. For the low score detection boxes, we utilize their similarities with tracklets to recover true objects and filter out the background detections. When applied to 9 different state-of-the-art trackers, our method achieves consistent improvement on IDF1 score ranging from 1 to 10 points. To put forwards the state-of-the-art performance of MOT, we design a simple and strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU. ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks. The source code, pre-trained models with deploy versions and tutorials of applying to other trackers are released at https://github.com/ifzhang/ByteTrack.},
	booktitle = {Computer {Vision} – {ECCV} 2022},
	author = {Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Weng, Fucheng and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
	editor = {Avidan, Shai and Brostow, Gabriel and Cissé, Moustapha and Farinella, Giovanni Maria and Hassner, Tal},
	year = {2022},
	keywords = {Data association, Detection boxes, Multi-object tracking},
	pages = {1--21},
}

@article{milan_mot16_2016-1,
	title = {{MOT16}: {A} {Benchmark} for {Multi}-{Object} {Tracking}},
	shorttitle = {{MOT16}},
	url = {http://arxiv.org/abs/1603.00831},
	abstract = {Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for reseach. Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods. The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. This paper accompanies a new release of the MOTChallenge benchmark. Unlike the initial release, all videos of MOT16 have been carefully annotated following a consistent protocol. Moreover, it not only offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest.},
	urldate = {2021-05-05},
	journal = {arXiv:1603.00831 [cs]},
	author = {Milan, Anton and Leal-Taixe, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
	month = may,
	year = {2016},
	note = {arXiv: 1603.00831},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{leal-taixe_motchallenge_2015-1,
	title = {{MOTChallenge} 2015: {Towards} a {Benchmark} for {Multi}-{Target} {Tracking}},
	shorttitle = {{MOTChallenge} 2015},
	url = {http://arxiv.org/abs/1504.01942},
	abstract = {In the recent past, the computer vision community has developed centralized benchmarks for the performance evaluation of a variety of tasks, including generic object and pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they have proved to be extremely helpful to advance the state of the art in the respective area. Interestingly, there has been rather limited work on the standardization of quantitative benchmarks for multiple target tracking. One of the few exceptions is the well-known PETS dataset, targeted primarily at surveillance applications. Despite being widely used, it is often applied inconsistently, for example involving using different subsets of the available data, different ways of training the models, or differing evaluation scripts. This paper describes our work toward a novel multiple object tracking benchmark aimed to address such issues. We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system. With MOTChallenge we aim to pave the way toward a unified evaluation framework for a more meaningful quantification of multi-target tracking.},
	urldate = {2019-08-07},
	journal = {arXiv:1504.01942 [cs]},
	author = {Leal-Taixé, Laura and Milan, Anton and Reid, Ian and Roth, Stefan and Schindler, Konrad},
	month = apr,
	year = {2015},
	note = {arXiv: 1504.01942},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@inproceedings{lin_microsoft_2014,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Microsoft {COCO}: {Common} {Objects} in {Context}},
	isbn = {978-3-319-10602-1},
	shorttitle = {Microsoft {COCO}},
	doi = {10.1007/978-3-319-10602-1_48},
	abstract = {We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.},
	language = {en},
	booktitle = {Computer {Vision} – {ECCV} 2014},
	publisher = {Springer International Publishing},
	author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, C. Lawrence},
	editor = {Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne},
	year = {2014},
	keywords = {Common Object, Object Category, Object Detection, Object Instance, Scene Understanding},
	pages = {740--755},
}

@article{rovamo_estimation_1979,
	title = {An estimation and application of the human cortical magnification factor},
	volume = {37},
	issn = {1432-1106},
	url = {https://doi.org/10.1007/BF00236819},
	doi = {10.1007/BF00236819},
	abstract = {Comparisons of the published data on the density D of receptive fields of retinal ganglion cells and on the cortical magnification factor M indicated that M2 is directly proportional to D in primates. Therefore, the human M can be estimated for the principal meridians of the visual field from the density-distribution of retinal ganglion cells and from the density of the centralmost cones. Using the previously published empirical data, we estimated the values of the human M and express the values in four simple equations that can be used for finding the value of M for any location of the visual field. The monocular values of M are not radially symmetric.},
	language = {en},
	number = {3},
	urldate = {2023-10-08},
	journal = {Experimental Brain Research},
	author = {Rovamo, J. and Virsu, V.},
	month = nov,
	year = {1979},
	keywords = {Contrast sensitivity, Cortical magnification factor, Man, Visual resolution},
	pages = {495--510},
}

@article{carrasco_eccentricity_1995,
	title = {The eccentricity effect: {Target} eccentricity affects performance on conjunction searches},
	volume = {57},
	issn = {1532-5962},
	shorttitle = {The eccentricity effect},
	url = {https://doi.org/10.3758/BF03208380},
	doi = {10.3758/BF03208380},
	abstract = {The serial pattern found for conjunction visual-search tasks has been attributed to covert attentional shifts, even though the possible contributions of target location have not been considered. To investigate the effect of target location on orientation × color conjunction searches, the target’s duration and its position in the display were manipulated. The display was present either until observers responded (Experiment 1), for 104 msec (Experiment 2), or for 62 msec (Experiment 3). Target eccentricity critically affected performance: A pronounced eccentricity effect was very similar for all three experiments; as eccentricity increased, reaction times and errors increased gradually. Furthermore, the set-size effect became more pronounced as target eccentricity increased, and the extent of the eccentricity effect increased for larger set sizes. In addition, according to stepwise regressions, target eccentricity as well as its interaction with set size were good predictors of performance. We suggest that these findings could be explained by spatial-resolution and lateral-inhibition factors. The serial self-terminating hypothesis for orientation × color conjunction searches was evaluated and rejected. We compared the eccentricity effect as well as the extent of the orientation asymmetry in these three conjunction experiments with those found in feature experiments (Carrasco \& Katz, 1992). The roles of eye movements, spatial resolution, and covert attention in the eccentricity effect, as well as their implications, are discussed.},
	language = {en},
	number = {8},
	urldate = {2023-10-08},
	journal = {Perception \& Psychophysics},
	author = {Carrasco, Marisa and Evert, Denise L. and Chang, Irene and Katz, Svetlana M.},
	month = nov,
	year = {1995},
	keywords = {Covert Attention, Feature Search, Retinal Eccentricity, Target Eccentricity, Visual Search},
	pages = {1241--1261},
}

@article{zelinsky_eye_2008,
	title = {An eye movement analysis of multiple object tracking in a realistic environment},
	volume = {16},
	issn = {1350-6285, 1464-0716},
	url = {http://www.tandfonline.com/doi/abs/10.1080/13506280802000752},
	doi = {10.1080/13506280802000752},
	language = {en},
	number = {5},
	urldate = {2023-07-06},
	journal = {Visual Cognition},
	author = {Zelinsky, Gregory J. and Neider, Mark B.},
	month = jul,
	year = {2008},
	pages = {553--566},
}

@article{meyerhoff_studying_2017,
	title = {Studying visual attention using the multiple object tracking paradigm: {A} tutorial review},
	volume = {79},
	issn = {1943-393X},
	shorttitle = {Studying visual attention using the multiple object tracking paradigm},
	url = {https://doi.org/10.3758/s13414-017-1338-1},
	doi = {10.3758/s13414-017-1338-1},
	abstract = {Human observers are capable of tracking multiple objects among identical distractors based only on their spatiotemporal information. Since the first report of this ability in the seminal work of Pylyshyn and Storm (1988, Spatial Vision, 3, 179–197), multiple object tracking has attracted many researchers. A reason for this is that it is commonly argued that the attentional processes studied with the multiple object paradigm apparently match the attentional processing during real-world tasks such as driving or team sports. We argue that multiple object tracking provides a good mean to study the broader topic of continuous and dynamic visual attention. Indeed, several (partially contradicting) theories of attentive tracking have been proposed within the almost 30 years since its first report, and a large body of research has been conducted to test these theories. With regard to the richness and diversity of this literature, the aim of this tutorial review is to provide researchers who are new in the field of multiple object tracking with an overview over the multiple object tracking paradigm, its basic manipulations, as well as links to other paradigms investigating visual attention and working memory. Further, we aim at reviewing current theories of tracking as well as their empirical evidence. Finally, we review the state of the art in the most prominent research fields of multiple object tracking and how this research has helped to understand visual attention in dynamic settings.},
	language = {en},
	number = {5},
	urldate = {2023-08-07},
	journal = {Attention, Perception, \& Psychophysics},
	author = {Meyerhoff, Hauke S. and Papenmeier, Frank and Huff, Markus},
	month = jul,
	year = {2017},
	keywords = {Dynamic attention, Multiple object tracking, Pylyshyn, Visual attention},
	pages = {1255--1274},
}

@article{kamkar_effectiveness_2020,
	title = {Effectiveness of “rescue saccades” on the accuracy of tracking multiple moving targets: {An} eye-tracking study on the effects of target occlusions},
	volume = {20},
	issn = {1534-7362},
	shorttitle = {Effectiveness of “rescue saccades” on the accuracy of tracking multiple moving targets},
	url = {https://doi.org/10.1167/jov.20.12.5},
	doi = {10.1167/jov.20.12.5},
	abstract = {Occlusion is one of the main challenges in tracking multiple moving objects. In almost all real-world scenarios, a moving object or a stationary obstacle occludes targets partially or completely for a short or long time during their movement. A previous study (Zelinsky \&amp; Todor, 2010) reported that subjects make timely saccades toward the object in danger of being occluded. Observers make these so-called “rescue saccades” to prevent target swapping. In this study, we examined whether these saccades are helpful. To this aim, we used as the stimuli recorded videos from natural movement of zebrafish larvae swimming freely in a circular container. We considered two main types of occlusion: object-object occlusions that naturally exist in the videos, and object-occluder occlusions created by adding a stationary doughnut-shape occluder in some videos. Four different scenarios were studied: (1) no occlusions, (2) only object-object occlusions, (3) only object-occluder occlusion, or (4) both object-object and object-occluder occlusions. For each condition, two set sizes (two and four) were applied. Participants’ eye movements were recorded during tracking, and rescue saccades were extracted afterward. The results showed that rescue saccades are helpful in handling object-object occlusions but had no reliable effect on tracking through object-occluder occlusions. The presence of occlusions generally increased visual sampling of the scenes; nevertheless, tracking accuracy declined due to occlusion.},
	number = {12},
	urldate = {2023-07-09},
	journal = {Journal of Vision},
	author = {Kamkar, Shiva and Abrishami Moghaddam, Hamid and Lashgari, Reza and Oksama, Lauri and Li, Jie and Hyönä, Jukka},
	month = nov,
	year = {2020},
	pages = {5},
}

@article{fehd_looking_2010,
	title = {Looking at the center of the targets helps multiple object tracking},
	volume = {10},
	issn = {1534-7362},
	url = {https://doi.org/10.1167/10.4.19},
	doi = {10.1167/10.4.19},
	abstract = {The ability to move our gaze to locations of interest facilitates interactions in everyday life. Where do participants direct gaze when multiple locations are of interest simultaneously? We previously demonstrated that, when tracking several moving targets amidst distractors in a multiple object tracking (MOT) task, participants primarily looked at a central point in between the targets (H. M. Fehd \&amp; A. E. Seiffert, 2008). This strategy of center-looking is in contrast to a target-looking strategy where participants would saccade from target to target. Here we investigated what factors influence the use of center-looking as well as its effectiveness. By decreasing object speed, we determined that center-looking is not a result of avoiding costly eye movements during tracking. Decreasing object size showed that peripheral visibility is necessary for tracking, but that center-looking continues up to the limits of peripheral visibility. Further analysis revealed that participants often engaged in both target-looking and center-looking by switching gaze from the center to targets and back again. Directly comparing participants' performance when they either did or did not include center-looking along with target-looking revealed that center-looking facilitates tracking performance. These results suggest that there is value in looking at the center that relates directly to the process of tracking multiple objects.},
	number = {4},
	urldate = {2023-07-08},
	journal = {Journal of Vision},
	author = {Fehd, Hilda M. and Seiffert, Adriane E.},
	month = apr,
	year = {2010},
	pages = {19},
}

@article{fehd_eye_2008,
	title = {Eye movements during multiple object tracking: {Where} do participants look?},
	volume = {108},
	issn = {0010-0277},
	shorttitle = {Eye movements during multiple object tracking},
	url = {https://www.sciencedirect.com/science/article/pii/S0010027707002776},
	doi = {10.1016/j.cognition.2007.11.008},
	abstract = {Similar to the eye movements you might make when viewing a sports game, this experiment investigated where participants tend to look while keeping track of multiple objects. While eye movements were recorded, participants tracked either 1 or 3 of 8 red dots that moved randomly within a square box on a black background. Results indicated that participants fixated closer to targets more often than to distractors. However, on 3-target trials, fixation was closer to the center of the triangle formed by the targets more often than to any individual targets. This center-looking strategy seemed to reflect that people were grouping the targets into a single object rather than simultaneously minimizing all target eccentricities. Here we find that observers deliberately focus their eyes on a location that is different from the objects they are attending, perhaps as a consequence of representing those objects as a group.},
	number = {1},
	urldate = {2023-10-08},
	journal = {Cognition},
	author = {Fehd, Hilda M. and Seiffert, Adriane E.},
	month = jul,
	year = {2008},
	keywords = {Attention, Eye movements, Tracking},
	pages = {201--209},
}

@article{upadhyayula_model_2020,
	title = {A model that adopts human fixations explains individual differences in multiple object tracking},
	volume = {205},
	issn = {1873-7838},
	doi = {10.1016/j.cognition.2020.104418},
	abstract = {In many settings "keep your eye on the ball" is good advice. People fixate important objects to obtain high quality information. Perhaps equally often, however, we engage with multiple important, moving, and unpredictable objects. Where should we fixate in these situations, and where do we? Do we for example appropriately center fixations to manage spatial non-uniformity in our visual system? And do we fixate empty space strategically to gain as much information as possible about multiple objects of interest? We explored these issues in the context of Multiple Object Tracking (MOT), wherein observers track several moving objects (targets) within a larger set of moving objects (nontargets), all the objects physically indistinguishable from one another. Among the features that make MOT an interesting paradigm is that it cannot be accommodated by continuous gaze to one important object, because there are multiple such objects in a given trial. Instead, it demands sustained processing of inputs from an entire display and iterated inferences about target versus nontarget identities. MOT therefore demands a strategic interaction between eye movements and cognition: the observer should seek fixation locations that minimize the aggregate probability of confusing any target with any nontarget. Individuals who meet this fixation challenge should perform the task better than those who meet the challenge less effectively. Here we describe a probabilistic model that implements the basic computations needed to do MOT, estimating the positions of targets, predicting their future positions, and inferring correspondences between new inputs and represented targets. The quality of the input received by the model depends on its fixation location at a given moment. We simulated a group of fifty participants who all performed the same MOT trials, with the model adopting each observer's fixation locations in the respective simulations. The model reliably predicted individual participant tracking performances and their relative rankings within the cohort. The results suggest that an individual's relative capability in this cognitively demanding task is in part determined by his/her utilization of eye fixations to control the quality and relevance of incoming visual input.},
	language = {eng},
	journal = {Cognition},
	author = {Upadhyayula, Aditya and Flombaum, Jonathan},
	month = dec,
	year = {2020},
	pmid = {32838957},
	keywords = {Attention, Computational modelling, Eye Movements, Eye tracking, Female, Fixation, Ocular, Humans, Individual differences, Individuality, Kalman filter, Male, Motion Perception, Multiple object tracking},
	pages = {104418},
}

@article{hyona_eye_2019,
	title = {Eye {Behavior} {During} {Multiple} {Object} {Tracking} and {Multiple} {Identity} {Tracking}},
	volume = {3},
	issn = {2411-5150},
	doi = {10.3390/vision3030037},
	abstract = {We review all published eye-tracking studies to date that have used eye movements to examine multiple object (MOT) or multiple identity tracking (MIT). In both tasks, observers dynamically track multiple moving objects. In MOT the objects are identical, whereas in MIT they have distinct identities. In MOT, observers prefer to fixate on blank space, which is often the center of gravity formed by the moving targets (centroid). In contrast, in MIT observers have a strong preference for the target-switching strategy, presumably to refresh and maintain identity-location bindings for the targets. To account for the qualitative differences between MOT and MIT, two mechanisms have been posited, a position tracking (MOT) and an identity tracking (MOT \& MIT) mechanism. Eye-tracking studies of MOT have also demonstrated that observers execute rescue saccades toward targets in danger of becoming occluded or are about to change direction after a collision. Crowding attracts the eyes close to it in order to increase visual acuity for the crowded objects to prevent target loss. It is suggested that future studies should concentrate more on MIT, as MIT more closely resembles tracking in the real world.},
	language = {eng},
	number = {3},
	journal = {Vision (Basel, Switzerland)},
	author = {Hyönä, Jukka and Li, Jie and Oksama, Lauri},
	month = jul,
	year = {2019},
	pmid = {31735838},
	pmcid = {PMC6802796},
	keywords = {dynamic attention, eye movements, multiple identity tracking, multiple object tracking},
	pages = {37},
}

@inproceedings{peters_flying_2022,
	address = {San Francisco},
	title = {Flying {Objects}: {Challenging} humans and machines in dynamic object vision},
	copyright = {All rights reserved},
	shorttitle = {Flying {Objects}},
	url = {https://2022.ccneuro.org/view_paper.php?PaperNum=1301},
	doi = {10.32470/CCN.2022.1301-0},
	language = {en},
	urldate = {2022-10-20},
	booktitle = {2022 {Conference} on {Cognitive} {Computational} {Neuroscience}},
	publisher = {Cognitive Computational Neuroscience},
	author = {Peters, Benjamin and Retchin, Matthew and Kriegeskorte, Nikolaus},
	year = {2022},
}

@article{peters_capturing_2021,
	title = {Capturing the objects of vision with neural networks},
	volume = {5},
	copyright = {2021 Springer Nature Limited},
	issn = {2397-3374},
	url = {https://www.nature.com/articles/s41562-021-01194-6},
	doi = {10.1038/s41562-021-01194-6},
	abstract = {Human visual perception carves a scene at its physical joints, decomposing the world into objects, which are selectively attended, tracked and predicted as we engage our surroundings. Object representations emancipate perception from the sensory input, enabling us to keep in mind that which is out of sight and to use perceptual content as a basis for action and symbolic cognition. Human behavioural studies have documented how object representations emerge through grouping, amodal completion, proto-objects and object files. By contrast, deep neural network models of visual object recognition remain largely tethered to sensory input, despite achieving human-level performance at labelling objects. Here, we review related work in both fields and examine how these fields can help each other. The cognitive literature provides a starting point for the development of new experimental tasks that reveal mechanisms of human object perception and serve as benchmarks driving the development of deep neural network models that will put the object into object recognition.},
	language = {en},
	number = {9},
	urldate = {2022-05-23},
	journal = {Nature Human Behaviour},
	author = {Peters, Benjamin and Kriegeskorte, Nikolaus},
	month = sep,
	year = {2021},
	note = {Number: 9
Publisher: Nature Publishing Group},
	keywords = {Human behaviour, Object vision, Perception, Psychology, Science, technology and society},
	pages = {1127--1144},
}

@inproceedings{peters_how_2022,
	title = {How do humans and machine learning models track multiple objects through occlusion?},
	copyright = {All rights reserved},
	url = {https://openreview.net/forum?id=n5OTU5qcj05},
	abstract = {Interacting with a complex environment often requires us to track multiple task-relevant objects not all of which are continually visible. The cognitive literature has focused on tracking a subset of visible identical abstract objects (e.g., circles), isolating the tracking component from its context in real-world experience. In the real world, object tracking is harder in that objects may not be continually visible and easier in that objects differ in appearance and so their recognition can rely on both remembered position and current appearance. Here we introduce a generalized task that combines tracking and recognition of valued objects that move in complex trajectories and frequently disappear behind occluders. Humans and models (from the computer-vision literature on object tracking) performed tasks varying widely in terms of the number of objects to be tracked, the number of distractors, the presence of an occluder, and the appearance similarity between targets and distractors. We replicated results from the human literature, including a deterioration of tracking performance with the number and similarity of targets and distractors. In addition, we find that increasing levels of occlusion reduce performance. All models tested here behaved in qualitatively different ways from human observers, showing superhuman performance for large numbers of targets, and subhuman performance under conditions of occlusion. Our framework will enable future studies to connect the human behavioral and engineering literatures, so as to test image-computable multiple-object-tracking models as models of human performance and to investigate how tracking and recognition interact under natural conditions of dynamic motion and occlusion.},
	language = {en},
	urldate = {2023-07-07},
	author = {Peters, Benjamin and Butkus, Eivinas and Kriegeskorte, Nikolaus},
	month = oct,
	year = {2022},
}

@inproceedings{wojke_simple_2017,
	title = {Simple online and realtime tracking with a deep association metric},
	booktitle = {2017 {IEEE} international conference on image processing ({ICIP})},
	publisher = {IEEE},
	author = {Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
	year = {2017},
	pages = {3645--3649},
}

@inproceedings{bewley_simple_2016,
	title = {Simple online and realtime tracking},
	url = {https://ieeexplore.ieee.org/document/7533003},
	doi = {10.1109/ICIP.2016.7533003},
	booktitle = {2016 {IEEE} {International} {Conference} on {Image} {Processing} ({ICIP})},
	publisher = {IEEE},
	author = {Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben},
	year = {2016},
	pages = {3464--3468},
}

@inproceedings{wojke_simple_2017-1,
	title = {Simple online and realtime tracking with a deep association metric},
	url = {https://ieeexplore.ieee.org/document/8296962},
	doi = {10.1109/ICIP.2017.8296962},
	booktitle = {2017 {IEEE} international conference on image processing ({ICIP})},
	publisher = {IEEE},
	author = {Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
	year = {2017},
	pages = {3645--3649},
}

@article{dumoulin_population_2008,
	title = {Population receptive field estimates in human visual cortex},
	volume = {39},
	issn = {10538119},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17977024},
	doi = {10.1016/j.neuroimage.2007.09.034},
	abstract = {We introduce functional MRI methods for estimating the neuronal population receptive field (pRF). These methods build on conventional visual field mapping that measures responses to ring and wedge patterns shown at a series of visual field locations and estimates the single position in the visual field that produces the largest response. The new method computes a model of the population receptive field from responses to a wide range of stimuli and estimates the visual field map as well as other neuronal population properties, such as receptive field size and laterality. The visual field maps obtained with the pRF method are more accurate than those obtained using conventional visual field mapping, and we trace with high precision the visual field maps to the center of the foveal representation. We report quantitative estimates of pRF size in medial, lateral and ventral occipital regions of human visual cortex. Also, we quantify the amount of input from ipsi- and contralateral visual fields. The human pRF size estimates in V1-V3 agree well with electrophysiological receptive field measurements at a range of eccentricities in corresponding locations within monkey and human visual field maps. The pRF method is non-invasive and can be applied to a wide range of conditions when it is useful to link fMRI signals in the visual pathways to neuronal receptive fields. © 2007 Elsevier Inc. All rights reserved.},
	language = {eng},
	number = {2},
	journal = {NeuroImage},
	author = {Dumoulin, Serge O. and Wandell, Brian A.},
	year = {2008},
	pmid = {17977024},
	note = {Edition: 2007/11/03
ISBN: 1053-8119 (Print){\textbackslash}n1053-8119 (Linking)},
	keywords = {Neuron, Retinotopic Mapping, V1, Visual areas, Visual cortex, fMRI, pRF},
	pages = {647--660},
}

@article{carrasco_cortical_1997,
	title = {Cortical {Magnification} {Neutralizes} the {Eccentricity} {Effect} in {Visual} {Search}},
	volume = {37},
	issn = {0042-6989},
	url = {https://www.sciencedirect.com/science/article/pii/S0042698996001022},
	doi = {10.1016/S0042-6989(96)00102-2},
	abstract = {We report two visual search experiments that explain an eccentricity effect previously found: detection of both feature and conjunction targets becomes increasingly less efficient as the orientation target appears at more distant field eccentricities (Carrasco et al., 1995). By cortically magnifying the stimuli we flattened out this effect for both feature and conjunction tasks. We conclude that spatial resolution factors affect visual search findings that have hitherto been attributed to covert attention. We stress the importance of analyzing data by target position to minimize the confound of the set size effect and retinal/field eccentricity. An alternative theory of orientation asymmetries is offered. Copyright © 1996 Elsevier Science Ltd.},
	number = {1},
	urldate = {2023-09-12},
	journal = {Vision Research},
	author = {Carrasco, MARISA and Frieder, KAREN S.},
	month = jan,
	year = {1997},
	keywords = {Visual search},
	pages = {63--82},
}

@article{strasburger_peripheral_2011,
	title = {Peripheral vision and pattern recognition: {A} review},
	volume = {11},
	issn = {1534-7362},
	shorttitle = {Peripheral vision and pattern recognition},
	url = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/11.5.13},
	doi = {10.1167/11.5.13},
	abstract = {We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantiﬁcations of the cortical magniﬁcation hypothesis, including an extension of Schwartz’s cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma’s law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricitydependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as signiﬁcant as those imposed on low-level functions and by way of crowding.},
	language = {en},
	number = {5},
	urldate = {2023-09-12},
	journal = {Journal of Vision},
	author = {Strasburger, H. and Rentschler, I. and Juttner, M.},
	month = dec,
	year = {2011},
	pages = {13--13},
}

@book{lindeberg_foveal_1994,
	title = {Foveal scale-space and the linear increase of receptive field size as a function of eccentricity},
	url = {https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-58890},
	abstract = {DiVA portal is a finding tool for research publications and student theses written at the following 50 universities and research institutions.},
	language = {eng},
	urldate = {2023-09-12},
	publisher = {KTH Royal Institute of Technology},
	author = {Lindeberg, Tony and Florack, Luc},
	year = {1994},
}

@misc{remmelzwaal_human_2019,
	title = {Human eye inspired log-polar pre-processing for neural networks},
	url = {http://arxiv.org/abs/1911.01141},
	abstract = {In this paper we draw inspiration from the human visual system, and present a bio-inspired pre-processing stage for neural networks. We implement this by applying a log-polar transformation as a pre-processing step, and to demonstrate, we have used a naive convolutional neural network (CNN). We demonstrate that a bio-inspired pre-processing stage can achieve rotation and scale robustness in CNNs. A key point in this paper is that the CNN does not need to be trained to identify rotation or scaling permutations; rather it is the log-polar pre-processing step that converts the image into a format that allows the CNN to handle rotation and scaling permutations. In addition we demonstrate how adding a log-polar transformation as a pre-processing step can reduce the image size to {\textasciitilde}20{\textbackslash}\% of the Euclidean image size, without significantly compromising classification accuracy of the CNN. The pre-processing stage presented in this paper is modelled after the retina and therefore is only tested against an image dataset. Note: This paper has been submitted for SAUPEC/RobMech/PRASA 2020.},
	language = {en},
	urldate = {2023-09-06},
	publisher = {arXiv},
	author = {Remmelzwaal, Leendert A. and Mishra, Amit and Ellis, George F. R.},
	month = nov,
	year = {2019},
	note = {arXiv:1911.01141 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
}

@article{florack_canonical_2014,
	title = {Canonical {Coordinates} for {Retino}-{Cortical} {Magnification}},
	volume = {3},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2075-1680},
	url = {https://www.mdpi.com/2075-1680/3/1/70},
	doi = {10.3390/axioms3010070},
	abstract = {A geometric model for a biologically-inspired visual front-end is proposed, based on an isotropic, scale-invariant two-form field. The model incorporates a foveal property typical of biological visual systems, with an approximately linear decrease of resolution as a function of eccentricity, and by a physical size constant that measures the radius of the geometric foveola, the central region characterized by maximal resolving power. It admits a description in singularity-free canonical coordinates generalizing the familiar log-polar coordinates and reducing to these in the asymptotic case of negligibly-sized geometric foveola or, equivalently, at peripheral locations in the visual field. It has predictive power to the extent that quantitative geometric relationships pertaining to retino-cortical magnification along the primary visual pathway, such as receptive field size distribution and spatial arrangement in retina and striate cortex, can be deduced in a principled manner. The biological plausibility of the model is demonstrated by comparison with known facts of human vision.},
	language = {en},
	number = {1},
	urldate = {2023-09-06},
	journal = {Axioms},
	author = {Florack, Luc},
	month = mar,
	year = {2014},
	note = {Number: 1
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {foveal vision, log-polar coordinates, resolution, retino-cortical magnification},
	pages = {70--81},
}

@misc{noauthor_log-polar_nodate,
	title = {Log-{Polar} mapping},
	url = {http://users.isr.ist.utl.pt/~alex/Projects/TemplateTracking/logpolar.htm},
	urldate = {2023-09-06},
}

@article{javier_traver_review_2010,
	title = {A review of log-polar imaging for visual perception in robotics},
	volume = {58},
	issn = {09218890},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0921889009001687},
	doi = {10.1016/j.robot.2009.10.002},
	abstract = {Log-polar imaging consists of a type of methods that represent visual information with a space-variant resolution inspired by the visual system of mammals. It has been studied for about three decades and has surpassed conventional approaches in robotics applications, mainly the ones where real-time constraints make it necessary to utilize resource-economic image representations and processing methodologies. This paper surveys the application of log-polar imaging in robotic vision, particularly in visual attention, target tracking, egomotion estimation, and 3D perception. The concise yet comprehensive review offered in this paper is intended to provide novel and experienced roboticists with a quick and gentle overview of log-polar vision and to motivate vision researchers to investigate the many open problems that still need solving. To help readers identify promising research directions, a possible research agenda is outlined. Finally, since log-polar vision is not restricted to robotics, a couple of other areas of application are discussed.},
	language = {en},
	number = {4},
	urldate = {2023-09-06},
	journal = {Robotics and Autonomous Systems},
	author = {Javier Traver, V. and Bernardino, Alexandre},
	month = apr,
	year = {2010},
	pages = {378--398},
}

@article{solari_design_2012,
	title = {Design strategies for direct multi-scale and multi-orientation feature extraction in the log-polar domain},
	volume = {33},
	issn = {0167-8655},
	url = {https://www.sciencedirect.com/science/article/pii/S0167865511002947},
	doi = {10.1016/j.patrec.2011.09.021},
	abstract = {Despite the well known advantages that a space-variant representation of the visual signal offers, the required adaptation of the algorithms developed in the Cartesian domain, before applying them in the log-polar space, has limited a wide use of such representation in visual processing applications. In this paper, we present a set of original rules for designing a discrete log-polar mapping that allows a direct application in the log-polar domain of the algorithms, based on spatial multi-scale and multi-orientation filtering, originally developed for the Cartesian domain. The advantage of the approach is to gain, without modifications, an effective space-variance and data reduction. Such design strategies are based on a quantitative analysis of the relationships between the spatial filtering and the space-variant representation. We assess the devised rules by using a distributed approach based on a bank of band-pass filters to compute reliable disparity maps, by providing quantitative measures of the computational load and of the accuracy of the computed visual features.},
	number = {1},
	urldate = {2023-09-06},
	journal = {Pattern Recognition Letters},
	author = {Solari, Fabio and Chessa, Manuela and Sabatini, Silvio P.},
	month = jan,
	year = {2012},
	keywords = {Design criteria, Disparity computation, Foveated images, Space-variant processing, Spatial filtering, Visual processing},
	pages = {41--51},
}

@article{schwartz_spatial_1977,
	title = {Spatial mapping in the primate sensory projection: {Analytic} structure and relevance to perception},
	volume = {25},
	issn = {1432-0770},
	shorttitle = {Spatial mapping in the primate sensory projection},
	url = {https://doi.org/10.1007/BF01885636},
	doi = {10.1007/BF01885636},
	abstract = {The retinotopic mapping of the visual field to the surface of the striate cortex is characterized as a longarithmic conformal mapping. This summarizes in a concise way the observed curve of cortical magnification, the linear scaling of receptive field size with eccentricity, and the mapping of global visual field landmarks. It is shown that if this global structure is reiterated at the local level, then the sequence regularity of the simple cells of area 17 may be accounted for as well. Recently published data on the secondary visual area, the medial visual area, and the inferior pulvinar of the owl monkey suggests that same global logarithmic structure holds for these areas as well. The available data on the structure of the somatotopic mapping (areaS-1) supports a similar analysis. The possible relevance of the analytical form of the cortical receptotopic maps to perception is examined and a brief discussion of the developmental implications of these findings is presented.},
	language = {en},
	number = {4},
	urldate = {2023-09-06},
	journal = {Biological Cybernetics},
	author = {Schwartz, E. L.},
	month = dec,
	year = {1977},
	keywords = {Conformal Mapping, Receptive Field, Simple Cell, Visual Area, Visual Field},
	pages = {181--194},
}

@inproceedings{larochelle_learning_2010,
	title = {Learning to combine foveal glimpses with a third-order {Boltzmann} machine},
	volume = {23},
	url = {https://proceedings.neurips.cc/paper/2010/hash/677e09724f0e2df9b6c000b75b5da10d-Abstract.html},
	abstract = {We describe a model based on a Boltzmann machine with third-order connections that can learn how to accumulate information about a shape over several fixations. The model uses a retina that only has enough high resolution pixels to cover a small area of the image, so it must decide on a sequence of fixations and it must combine the glimpse" at each fixation with the location of the fixation before integrating the information with information from other glimpses of the same object. We evaluate this model on a synthetic dataset and two image classification datasets, showing that it can perform at least as well as a model trained on whole images."},
	urldate = {2023-09-06},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Larochelle, Hugo and Hinton, Geoffrey E},
	year = {2010},
}

@techreport{standvoss_visual_2020,
	type = {preprint},
	title = {Visual {Attention} {Through} {Uncertainty} {Minimization} in {Recurrent} {Generative} {Models}},
	url = {http://biorxiv.org/lookup/doi/10.1101/2020.02.14.948992},
	abstract = {Abstract
          Allocating visual attention through saccadic eye movements is a key ability of intelligent agents. Attention is both influenced through bottom-up stimulus properties as well as top-down task demands. The interaction of these two attention mechanisms is not yet fully understood. A parsimonious reconciliation posits that both processes serve the minimization of predictive uncertainty. We propose a recurrent generative neural network model that predicts a visual scene based on foveated glimpses. The model shifts its attention in order to minimize the uncertainty in its predictions. We show that the proposed model produces naturalistic eye movements focusing on informative stimulus regions. Introducing additional tasks modulates the saccade patterns towards task-relevant stimulus regions. The model’s saccade characteristics correspond well with previous experimental data in humans, providing evidence that uncertainty minimization could be a fundamental mechanisms for the allocation of visual attention.},
	language = {en},
	urldate = {2020-03-06},
	institution = {Neuroscience},
	author = {Standvoss, Kai and Quax, Silvan C. and van Gerven, Marcel A.J.},
	month = feb,
	year = {2020},
	doi = {10.1101/2020.02.14.948992},
}

@inproceedings{mnih_recurrent_2014,
	title = {Recurrent {Models} of {Visual} {Attention}},
	volume = {27},
	url = {https://proceedings.neurips.cc/paper_files/paper/2014/hash/09c6c3783b4a70054da74f2538ed47c6-Abstract.html},
	abstract = {Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution. Like convolutional neural networks, the proposed model has a degree of translation invariance built-in, but the amount of computation it performs can be controlled independently of the input image size. While the model is non-differentiable, it can be trained using reinforcement learning methods to learn task-specific policies. We evaluate our model on several image classification tasks, where it significantly outperforms a convolutional neural network baseline on cluttered images, and on a dynamic visual control problem, where it learns to track a simple object without an explicit training signal for doing so.},
	urldate = {2023-09-06},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Mnih, Volodymyr and Heess, Nicolas and Graves, Alex and kavukcuoglu, koray},
	year = {2014},
}
