@article{lund2007importance,
  title={The importance of gaze and gesture in interactive multimodal explanation},
  author={Lund, Kristine},
  journal={Language Resources and Evaluation},
  volume={41},
  pages={289--303},
  year={2007},
  publisher={Springer}
}

@inproceedings{tu2022end,
  title={End-to-end human-gaze-target detection with transformers},
  author={Tu, Danyang and Min, Xiongkuo and Duan, Huiyu and Guo, Guodong and Zhai, Guangtao and Shen, Wei},
  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={2192--2200},
  year={2022},
  organization={IEEE}
}

@article{itti2001computational,
  title={Computational modelling of visual attention},
  author={Itti, Laurent and Koch, Christof},
  journal={Nature reviews neuroscience},
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  pages={194--203},
  year={2001},
  publisher={Nature Publishing Group UK London}
}

@InProceedings{Judd_2009,
  author    = {Tilke Judd and Krista Ehinger and Fr{\'e}do Durand and Antonio Torralba},
  title     = {Learning to Predict Where Humans Look},
  booktitle = {IEEE International Conference on Computer Vision (ICCV)},
  year      = {2009}
}


@inproceedings{bao2022escnet,
  title={Escnet: Gaze target detection with the understanding of 3d scenes},
  author={Bao, Jun and Liu, Buyu and Yu, Jun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14126--14135},
  year={2022}
}

INPROCEEDINGS{dualattentionguidedgazetarget,
  author={Fang, Yi and Tang, Jiapeng and Shen, Wang and Shen, Wei and Gu, Xiao and Song, Li and Zhai, Guangtao},
  booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Dual Attention Guided Gaze Target Detection in the Wild}, 
  year={2021},
  volume={},
  number={},
  pages={11385-11394},
  doi={10.1109/CVPR46437.2021.01123}}

@misc{Authors14,
 author = {FirstName LastName},
 title = {The frobnicatable foo filter},
 note = {Face and Gesture submission ID 324. Supplied as supplemental material {\tt fg324.pdf}},
 year = 2014
}

@misc{Authors14b,
 author = {FirstName LastName},
 title = {Frobnication tutorial},
 note = {Supplied as supplemental material {\tt tr.pdf}},
 year = 2014
}

@article{Alpher02,
author = {FirstName Alpher},
title = {Frobnication},
journal = PAMI,
volume = 12,
number = 1,
pages = {234--778},
year = 2002
}

@article{Alpher03,
author = {FirstName Alpher and  FirstName Fotheringham-Smythe},
title = {Frobnication revisited},
journal = {Journal of Foo},
volume = 13,
number = 1,
pages = {234--778},
year = 2003
}

@article{Alpher04,
author = {FirstName Alpher and FirstName Fotheringham-Smythe and FirstName Gamow},
title = {Can a machine frobnicate?},
journal = {Journal of Foo},
volume = 14,
number = 1,
pages = {234--778},
year = 2004
}

@inproceedings{Alpher05,
author = {FirstName Alpher and FirstName Gamow},
title = {Can a computer frobnicate?},
booktitle = CVPR,
pages = {234--778},
year = 2005
}


@inproceedings{nips15_recasens,
 author = "Adria Recasens$^*$ and Aditya Khosla$^*$ and Carl Vondrick and Antonio Torralba",
 title = "Where are they looking?",
 booktitle = "Advances in Neural Information Processing Systems (NIPS)",
 year = "2015",
 note = "$^*$ indicates equal contribution"
}

@INPROCEEDINGS {9157393,
author = {E. Chong and Y. Wang and N. Ruiz and J. M. Rehg},
booktitle = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {Detecting Attended Visual Targets in Video},
year = {2020},
volume = {},
issn = {},
pages = {5395-5405},
abstract = {We address the problem of detecting attention targets in video. Our goal is to identify where each person in each frame of a video is looking, and correctly handle the case where the gaze target is out-of-frame. Our novel architecture models the dynamic interaction between the scene and head features and infers time-varying attention targets. We introduce a new annotated dataset, VideoAttentionTarget, containing complex and dynamic patterns of real-world gaze behavior. Our experiments show that our model can effectively infer dynamic attention in videos. In addition, we apply our predicted attention maps to two social gaze behavior recognition tasks, and show that the resulting classifiers significantly outperform existing methods. We achieve state-of-the-art performance on three datasets: GazeFollow (static images), VideoAttentionTarget (videos), and VideoCoAtt (videos), and obtain the first results for automatically classifying clinically-relevant gaze behavior without wearable cameras or eye trackers.},
keywords = {head;task analysis;target tracking;computer architecture;predictive models;visualization;three-dimensional displays},
doi = {10.1109/CVPR42600.2020.00544},
url = {https://doi.ieeecomputersociety.org/10.1109/CVPR42600.2020.00544},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {jun}
}


@article{Chen2016,
   abstract = {In recent years, with increase in the number of vehicles, more and more traffic issues are becoming the focus of attention worldwide. One of the most important problems is traffic congestion. Currently, most traffic systems still use fixed-Time setting for a very long cycle. These systems cannot dynamically adjust traffic light timing in response to unexpected situations such as traffic accidents, natural calamities, or sudden incidents. With advances in technology, traffic data such as traffic volume, speed, and waiting time can now be gathered by sensors or cameras. Leveraging on automatic traffic data collection we try to address the traffic congestion issue by proposing a novel Model Predictive Control (MPC)-based Traffic Light Control System (MTLCS). MTLCS includes a traffic flow prediction model and a traffic timing optimization method. Historical traffic data is used to predict future traffic volumes. An MPC-based traffic light optimization method is proposed to obtain appropriate time settings that can reduce overall congestion. Our method also has the ability to dynamically adjust traffic signal timings. It can rapidly respond to real-Time traffic conditions to reduce traffic congestion. Experiments show that the Mean Absolute Percentage Error (MAPE) of our traffic flow prediction model is less than 12%. Using the proposed MPC-based optimization method, the average waiting rate is reduced by 29.70%, when compared with the fixed-Time traffic light control system.},
   author = {You Ren Chen and Keng Pin Chen and Pao Ann Hsiung},
   doi = {10.1109/ITSC.2016.7795937},
   isbn = {9781509018895},
   journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
   pages = {2366-2371},
   title = {Dynamic traffic light optimization and control system using model-predictive control method},
   year = {2016},
}
@article{,
   abstract = {Traffic simulation is a widely used method applied in the research on traffic modelling, planning and development of traffic networks and systems. From the literature study, a variety traffic simulation models were found in experiments and applications with aims to imaginary real traffic operations. The traffic simulation models can be categorised into three namely, microscopic modelling, macroscopic modelling and mesoscopic modelling. This report is aimed to overview these traffic simulation models, in term of its function, limitation and application.},
   author = {Nurul Nasuha Nor Azlan and Munzilah Md Rohani},
   doi = {10.1051/matecconf/201815003006},
   isbn = {2018150030},
   issn = {2261236X},
   journal = {MATEC Web of Conferences},
   pages = {1-6},
   title = {Overview of Application of Traffic Simulation Model},
   volume = {150},
   year = {2018},
}

@article{Nishi2018,
   abstract = {Traffic signal control can mitigate traffic congestion and reduce travel time. A model-free reinforcement learning (RL) approach is a powerful framework for learning a responsive traffic control policy for short-term traffic demand changes without prior environmental knowledge. Previous RL approaches could handle high-dimensional feature space using a standard neural network, e.g., a convolutional neural network; however, to control traffic on a road network with multiple intersections, the geometric features between roads had to be created manually. Rather than using manually crafted geometric features, we developed an RL-based traffic signal control method that employs a graph convolutional neural network (GCNN). GCNNs can automatically extract features considering the traffic features between distant roads by stacking multiple neural network layers. We numerically evaluated the proposed method in a six-intersection environment. The results demonstrate that the proposed method can find comparable policies twice as fast as the conventional RL method with a neural network and can adapt to more extensive traffic demand changes.},
   author = {Tomoki Nishi and Keisuke Otaki and Keiichiro Hayakawa and Takayoshi Yoshimura},
   doi = {10.1109/ITSC.2018.8569301},
   isbn = {9781728103235},
   journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
   pages = {877-883},
   title = {Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets},
   volume = {2018-Novem},
   year = {2018},
}
@article{Cui2020,
   abstract = {Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model's loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.},
   author = {Zhiyong Cui and Kristian Henrickson and Ruimin Ke and Yinhai Wang},
   doi = {10.1109/TITS.2019.2950416},
   issn = {15580016},
   issue = {11},
   journal = {IEEE Transactions on Intelligent Transportation Systems},
   keywords = {LSTM,Traffic forecasting,graph convolution,recurrent neural network,spatialoral},
   pages = {4883-4894},
   publisher = {IEEE},
   title = {Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting},
   volume = {21},
   year = {2020},
}

@inproceedings{8921091,
   abstract = {Biometric authentication is more and more popular these days. Among authentication techniques, face recognition is the most widely used technique. Face anti-spoofing is the core of the biometric system. Face Anti-spoofing is about prevention of spoofing attack by detecting the face image is live or not before feeding it to the system. In this paper, we propose two streamed line approaches for face anti-spoofing. First approach is detecting of eyes movement and second approach is CNN-based liveness detection by extracting the local features. Experiment demonstrates on comparison of previous works with HTER (Half Total Error Rate) over three datasets: NUAA imposter dataset, Replay Attack and OWN replay dataset which is created in this paper.},
   author = {P P P Linn and E C Htoon},
   doi = {10.1109/AITC.2019.8921091},
   journal = {2019 International Conference on Advanced Information Technologies (ICAIT)},
   keywords = {CNN-based liveness detection,Challenge-Response Method,Convolutional Neural Network,Convolutional neural networks,Face,Face Anti-spoofing,Feature extraction,HTER,Machine Learning,SIFT,Streaming media,Testing,Training,Training data,authentication techniques,biometric authentication,biometric system,biometrics (access control),convolutional neural nets,eyes movement,face anti-spoofing,face image,face recognition,feature extraction,half total error rate},
   month = {11},
   pages = {149-154},
   title = {Face Anti-spoofing using Eyes Movement and CNN-based Liveness Detection},
   year = {2019},
}


@article{jefferson2019usability,
   author = {Madeline Jefferson},
   title = {Usability of automatic speech recognition systems for individuals with speech disorders: Past, present, future, and a proposed model},
   year = {2019},
}
@generic{D1931,
   abstract = {Chabaud, D., & Codron, J. M. (2005). How to integrate the specificities of some food departments into a retail store organization? Lessons and limits of the Aokian theory of the firm. International Journal of Retail & Distribution Management, 33(8), 597-617. Chicago},
   author = {Sokoloff D},
   pages = {167-168},
   title = {Scholar (8)},
   year = {1931},
}
@inproceedings{Yan2021,
   author = {Yichao Yan and Jinpeng Li and Jie Qin and Song Bai and Shengcai Liao and Li Liu and Fan Zhu and Ling Shao},
   journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
   pages = {7690-7699},
   title = {Anchor-Free Person Search},
   year = {2021},
}
@article{Chu2021,
   author = {Peng Chu and Jiang Wang and Quanzeng You and Haibin Ling and Zicheng Liu},
   journal = {arXiv preprint arXiv:2104.00194},
   title = {TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking},
   year = {2021},
}
@article{Jin2021,
   author = {Zengrui Jin and Mengzhe Geng and Xurong Xie and Jianwei Yu and Shansong Liu and Xunying Liu and Helen Meng},
   journal = {arXiv preprint arXiv:2108.00899},
   title = {Adversarial Data Augmentation for Disordered Speech Recognition},
   year = {2021},
}

@article{Pham2017,
   abstract = {Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden – diabetes and mental health – the results show improved prediction accuracy.},
   author = {Trang Pham and Truyen Tran and Dinh Phung and Svetha Venkatesh},
   doi = {https://doi.org/10.1016/j.jbi.2017.04.001},
   issn = {1532-0464},
   journal = {Journal of Biomedical Informatics},
   keywords = {Electronic medical records,Healthcare processes,Irregular timing,Long-Short Term Memory,Predictive medicine},
   pages = {218-229},
   title = {Predicting healthcare trajectories from medical records: A deep learning approach},
   volume = {69},
   url = {https://www.sciencedirect.com/science/article/pii/S1532046417300710},
   year = {2017},
}
@article{Wang2020,
   author = {Yongxin Wang and Kris Kitani and Xinshuo Weng},
   journal = {arXiv preprint arXiv:2006.13164},
   title = {Joint object detection and multi-object tracking with graph neural networks},
   year = {2020},
}
@article{Zhang2021,
   author = {Yifu Zhang and Chunyu Wang and Xinggang Wang and Wenjun Zeng and Wenyu Liu},
   issn = {1573-1405},
   journal = {International Journal of Computer Vision},
   pages = {1-19},
   publisher = {Springer},
   title = {Fairmot: On the fairness of detection and re-identification in multiple object tracking},
   year = {2021},
}
@article{Hawley2013,
   author = {M S Hawley and S P Cunningham and P D Green and P Enderby and R Palmer and S Sehgal and P O'Neill},
   doi = {10.1109/TNSRE.2012.2209678},
   issn = {1558-0210 VO - 21},
   issue = {1},
   journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
   pages = {23-31},
   title = {A Voice-Input Voice-Output Communication Aid for People With Severe Speech Impairment},
   volume = {21},
   year = {2013},
}
@article{Angelov2020,
   author = {Dimo Angelov},
   journal = {arXiv preprint arXiv:2008.09470},
   title = {Top2vec: Distributed representations of topics},
   year = {2020},
}
@inproceedings{Yan2021,
   author = {Yichao Yan and Jinpeng Li and Jie Qin and Song Bai and Shengcai Liao and Li Liu and Fan Zhu and Ling Shao},
   journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
   pages = {7690-7699},
   title = {Anchor-Free Person Search},
   year = {2021},
}

@article{Kannan2019,
   author = {Vinesh Kannan and Matthew A Shapiro and Mustafa Bilgic},
   journal = {arXiv preprint arXiv:1910.04906},
   title = {Hindsight Analysis of the Chicago Food Inspection Forecasting Model},
   year = {2019},
}
@inproceedings{Ahuja2021,
   author = {Karan Ahuja and Deval Shah and Sujeath Pareddy and Franceska Xhakaj and Amy Ogan and Yuvraj Agarwal and Chris Harrison},
   journal = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
   pages = {1-9},
   title = {Classroom Digital Twins with Instrumentation-Free Gaze Tracking},
   year = {2021},
}
@inproceedings{McMurrough2012,
   author = {Christopher D McMurrough and Vangelis Metsis and Jonathan Rich and Fillia Makedon},
   journal = {Proceedings of the Symposium on Eye Tracking Research and Applications},
   pages = {305-308},
   title = {An eye tracking dataset for point of gaze detection},
   year = {2012},
}

@article{10.1007/s00138-017-0852-4,
author = {Huang, Qiong and Veeraraghavan, Ashok and Sabharwal, Ashutosh},
title = {TabletGaze: Dataset and Analysis for Unconstrained Appearance-Based Gaze Estimation in Mobile Tablets},
year = {2017},
issue_date = {August    2017},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
volume = {28},
number = {5–6},
issn = {0932-8092},
url = {https://doi.org/10.1007/s00138-017-0852-4},
doi = {10.1007/s00138-017-0852-4},
abstract = {We study gaze estimation on tablets; our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet are not constrained. We collected a large unconstrained gaze dataset of tablet users, labeled Rice TabletGaze dataset. The dataset consists of 51 subjects, each with 4 different postures and 35 gaze locations. Subjects vary in race, gender and in their need for prescription glasses, all of which might impact gaze estimation accuracy. We made three major observations on the collected data and employed a baseline algorithm for analyzing the impact of several factors on gaze estimation accuracy. The baseline algorithm is based on multilevel HoG feature and Random Forests regressor, which achieves a mean error of 3.17 cm. We perform extensive evaluation on the impact of various practical factors such as person dependency, dataset size, race, wearing glasses and user posture on the gaze estimation accuracy.},
journal = {Mach. Vision Appl.},
month = {aug},
pages = {445–461},
numpages = {17},
keywords = {Eye, Gaze estimation/tracking, Applications, Mobile device, Dataset}
}

@article{Bidwell2011,
   author = {Jonathan Bidwell and Henry Fuchs},
   journal = {Behav Res Methods},
   pages = {113},
   title = {Classroom analytics: Measuring student engagement with automated gaze tracking},
   volume = {49},
   year = {2011},
}
@inproceedings{Zhou2019,
   author = {Kaiyang Zhou and Yongxin Yang and Andrea Cavallaro and Tao Xiang},
   journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
   pages = {3702-3712},
   title = {Omni-scale feature learning for person re-identification},
   year = {2019},
}
@inproceedings{Siegfried2021,
   author = {Rémy Siegfried and Jean-Marc Odobez},
   journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
   pages = {3153-3161},
   title = {Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets},
   year = {2021},
}
@inproceedings{Pervaiz2021,
   author = {M Pervaiz and A Jalal and K Kim},
   doi = {10.1109/IBCAST51254.2021.9393171},
   isbn = {2151-1411 VO -},
   journal = {2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST)},
   pages = {530-535},
   title = {Hybrid Algorithm for Multi People Counting and Tracking for Smart Surveillance},
   year = {2021},
}

@inproceedings{Le2020,
   author = {M C Le and M -H. Le and M -T. Duong},
   doi = {10.1109/GTSD50082.2020.9303117},
   isbn = {VO -},
   journal = {2020 5th International Conference on Green Technology and Sustainable Development (GTSD)},
   pages = {349-352},
   title = {Vision-based People Counting for Attendance Monitoring System},
   year = {2020},
}
@article{Renawi2021,
   author = {Abdulrahman Renawi and Fady Alnajjar and Medha Parambil and Zouheir Trabelsi and Munkhjargal Gochoo and Sumaya Khalid and Omar Mubin},
   issn = {1573-7608},
   journal = {Education and Information Technologies},
   pages = {1-18},
   publisher = {Springer},
   title = {A simplified real-time camera-based attention assessment system for classrooms: pilot study},
   year = {2021},
}
@inproceedings{Song2021,
   author = {Qingyu Song and Changan Wang and Zhengkai Jiang and Yabiao Wang and Ying Tai and Chengjie Wang and Jilin Li and Feiyue Huang and Yang Wu},
   journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
   pages = {3365-3374},
   title = {Rethinking counting and localization in crowds: A purely point-based framework},
   year = {2021},
}
@article{Shao2018,
   author = {Shuai Shao and Zijian Zhao and Boxun Li and Tete Xiao and Gang Yu and Xiangyu Zhang and Jian Sun},
   journal = {arXiv preprint arXiv:1805.00123},
   title = {Crowdhuman: A benchmark for detecting human in a crowd},
   year = {2018},
}
@article{Zhou2021,
   author = {Kaiyang Zhou and Yongxin Yang and Andrea Cavallaro and Tao Xiang},
   issn = {0162-8828},
   journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
   publisher = {IEEE},
   title = {Learning generalisable omni-scale representations for person re-identification},
   year = {2021},
}
@article{Sun2021,
   author = {Guolei Sun and Yun Liu and Thomas Probst and Danda Pani Paudel and Nikola Popovic and Luc Van Gool},
   journal = {arXiv preprint arXiv:2105.10926},
   title = {Boosting crowd counting with transformers},
   year = {2021},
}
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   author = {Xianhao Zhou and Songshan Guo and Chan Hou Che and Brenda Cheang and Andrew Lim and Hubert Kreuter and Janet Chow},
   journal = {Twenty-First IAAI Conference},
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   year = {2009},
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@article{Han2022,
   author = {Ligong Han and Jian Ren and Hsin-Ying Lee and Francesco Barbieri and Kyle Olszewski and Shervin Minaee and Dimitris Metaxas and Sergey Tulyakov},
   journal = {arXiv preprint arXiv:2203.02573},
   title = {Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning},
   year = {2022},
}
@inproceedings{Fabbri2021,
   author = {Matteo Fabbri and Guillem Brasó and Gianluca Maugeri and Orcun Cetintas and Riccardo Gasparini and Aljoša Ošep and Simone Calderara and Laura Leal-Taixé and Rita Cucchiara},
   journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
   pages = {10849-10859},
   title = {MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?},
   year = {2021},
}
@article{Minaee2021,
   author = {Shervin Minaee and Ping Luo and Zhe Lin and Kevin Bowyer},
   journal = {arXiv preprint arXiv:2103.14983},
   title = {Going deeper into face detection: A survey},
   year = {2021},
}
@article{Hartanti2019,
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   volume = {17},
   year = {2019},
}
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@article{Schirokoff2012,
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   author = {Xiaoshuang Li and Zhongzheng Guo and Xingyuan Dai and Yilun Lin and Junchen Jin and Fenghua Zhu and Fei-Yue Wang},
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@inproceedings{Jagadeesh2015,
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@article{Lam2016,
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   publisher = {IEEE},
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}
@article{Ali2019,
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   journal = {Transportation research part C: emerging technologies},
   pages = {50-71},
   publisher = {Elsevier},
   title = {On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment},
   volume = {111},
   year = {2020},
}

@inproceedings{Henry2019,
   author = {Elise Henry and Angelo Furno and Nour-Eddin El Faouzi},
   journal = {TRB 2019, 98th Annual Meeting Transportation Research Board},
   pages = {21p},
   title = {A graph-based approach with simulated traffic dynamics for the analysis of transportation resilience in smart cities},
   year = {2019},
}
@article{Qu2019,
   author = {Licheng Qu and Wei Li and Wenjing Li and Dongfang Ma and Yinhai Wang},
   issn = {0957-4174},
   journal = {Expert Systems with applications},
   pages = {304-312},
   publisher = {Elsevier},
   title = {Daily long-term traffic flow forecasting based on a deep neural network},
   volume = {121},
   year = {2019},
}
@article{Lin2021,
   author = {Feng-Cheng Lin and Huu-Huy Ngo and Chyi-Ren Dow and Ka-Hou Lam and Hung Linh Le},
   issn = {1424-8220},
   issue = {16},
   journal = {Sensors},
   pages = {5314},
   publisher = {MDPI},
   title = {Student behavior recognition system for the classroom environment based on skeleton pose estimation and person detection},
   volume = {21},
   year = {2021},
}
@article{Abdallah2021,
   author = {Taoufik Ben Abdallah and Islam Elleuch and Radhouane Guermazi},
   issn = {1877-0509},
   journal = {Procedia Computer Science},
   pages = {951-960},
   publisher = {Elsevier},
   title = {Student Behavior Recognition in Classroom using Deep Transfer Learning with VGG-16},
   volume = {192},
   year = {2021},
}
@article{Veronese2017,
   author = {Andrea Veronese and Mattia Racca and Roel Stephan Pieters and Ville Kyrki},
   issn = {2296-9144},
   journal = {Frontiers in Robotics and AI},
   pages = {53},
   publisher = {Frontiers Media SA},
   title = {Probabilistic Mapping of human Visual attention from head Pose estimation},
   volume = {4},
   year = {2017},
}

@article{Liang2022,
   author = {Chao Liang and Zhipeng Zhang and Xue Zhou and Bing Li and Shuyuan Zhu and Weiming Hu},
   issn = {1057-7149},
   journal = {IEEE Transactions on Image Processing},
   pages = {3182-3196},
   publisher = {IEEE},
   title = {Rethinking the competition between detection and ReID in multiobject tracking},
   volume = {31},
   year = {2022},
}
@article{Luo2019,
   author = {Hao Luo and Wei Jiang and Youzhi Gu and Fuxu Liu and Xingyu Liao and Shenqi Lai and Jianyang Gu},
   issn = {1520-9210},
   issue = {10},
   journal = {IEEE Transactions on Multimedia},
   pages = {2597-2609},
   publisher = {IEEE},
   title = {A strong baseline and batch normalization neck for deep person re-identification},
   volume = {22},
   year = {2019},
}
@inproceedings{Zhou2019,
   author = {Kaiyang Zhou and Yongxin Yang and Andrea Cavallaro and Tao Xiang},
   journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
   pages = {3702-3712},
   title = {Omni-scale feature learning for person re-identification},
   year = {2019},
}
@article{Zang2022,
   author = {Xianghao Zang and Ge Li and Wei Gao and Xiujun Shu},
   issn = {1751-9659},
   issue = {3},
   journal = {IET Image Processing},
   pages = {729-741},
   publisher = {Wiley Online Library},
   title = {Exploiting robust unsupervised video person re‐identification},
   volume = {16},
   year = {2022},
}
@inproceedings{Yucel2009,
   author = {Zeynep Yucel and Albert Ali Salah},
   journal = {Annual Meeting of Cognitive Science Society (CogSci 2009)},
   title = {Head Pose and Neural Network Based Gaze Direction Estimation for Joint Attention Modeling in Embodied Agents},
   year = {2009},
}

@article{Singh2021,
   author = {Tripti Singh and Mohan Mohadikar and Shilpa Gite and Shruti Patil and Biswajeet Pradhan and Abdullah Alamri},
   issn = {2169-3536},
   journal = {IEEE Access},
   pages = {142632-142643},
   publisher = {IEEE},
   title = {Attention Span Prediction Using Head-Pose Estimation With Deep Neural Networks},
   volume = {9},
   year = {2021},
}
@inproceedings{Eom2021,
   author = {Chanho Eom and Geon Lee and Junghyup Lee and Bumsub Ham},
   journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
   pages = {12036-12045},
   title = {Video-based person re-identification with spatial and temporal memory networks},
   year = {2021},
}
@inproceedings{Behera2020,
   author = {Ardhendu Behera and Zachary Wharton and Pradeep Hewage and Swagat Kumar},
   journal = {Proceedings of the Asian Conference on Computer Vision},
   title = {Rotation axis focused attention network (RAFA-Net) for estimating head pose},
   year = {2020},
}
@inproceedings{Ling2021,
   author = {Xufeng Ling and Dong Wang and Jie Yang},
   journal = {2021 7th International Conference on Computing and Artificial Intelligence},
   pages = {154-159},
   title = {A New Head Pose Estimation Method Using Vision Transformer Model},
   year = {2021},
}
@article{Pereira2022,
   author = {Ricardo Pereira and Guilherme Carvalho and Luís Garrote and Urbano J Nunes},
   issn = {2076-3417},
   issue = {3},
   journal = {Applied Sciences},
   pages = {1319},
   publisher = {MDPI},
   title = {Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics},
   volume = {12},
   year = {2022},
}
@article{Ji2020,
   author = {Qingge Ji and Haoqiang Yu and Xiao Wu},
   issn = {1999-4893},
   issue = {4},
   journal = {Algorithms},
   pages = {80},
   publisher = {MDPI},
   title = {Hierarchical-matching-based online and real-time multi-object tracking with deep appearance features},
   volume = {13},
   year = {2020},
}
@inproceedings{Ding2021,
   author = {L Ding and S Wang and R Li and L Chen and J Dong},
   doi = {10.1109/ICIVC52351.2021.9526965},
   journal = {2021 6th International Conference on Image, Vision and Computing (ICIVC)},
   pages = {66-71},
   title = {PC-PINet: Partial Re-identification Network for People Counting with Overlapping Cameras},
   year = {2021},
}
@inproceedings{Islam2022,
   author = {Atiqul Islam and Mark Kit Tsun Tee and Bee Theng Lau},
   journal = {Proceedings of the 2022 6th High Performance Computing and Cluster Technologies Conference},
   pages = {44-50},
   title = {Development of an Improved Occluded Person Re-Identification System Using Deep Learning},
   year = {2022},
}
@inproceedings{Touvron2021,
   author = {Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
   isbn = {2640-3498},
   journal = {International Conference on Machine Learning},
   pages = {10347-10357},
   publisher = {PMLR},
   title = {Training data-efficient image transformers & distillation through attention},
   year = {2021},
}
@article{Yang2022,
   author = {Feng Yang and Xingle Zhang and Bo Liu},
   journal = {arXiv preprint arXiv:2207.12202},
   title = {Video object tracking based on YOLOv7 and DeepSORT},
   year = {2022},
}
@inproceedings{Bachechi2022,
   abstract = {The road network is the infrastructure along which the mobility of users and goods takes place; the analysis of these networks in terms of spatial and graph theoretical approaches can provide insights to understand urban mobility, improve daily commuting, and reflect on new, more sustainable, scenarios. This paper presents an open-source framework to analyze the road network and investigate the relationship between its topology and traffic conditions. Open-source geographical data are stored in a graph database containing roads, junctions, and Points of Interest (POI), allowing importing of traffic data. The framework includes routing algorithms to obtain the optimal path based on different aspects such as distance, traffic volume, and the number of traversed junctions; furthermore, it allows simulating road closures to observe how they affect road viability. The framework was tested in the use case of the city of Modena (Italy) providing promising results.},
   author = {Chiara Bachechi and Laura Po},
   city = {Cham},
   editor = {Silvia Chiusano and Tania Cerquitelli and Robert Wrembel},
   isbn = {978-3-031-15740-0},
   journal = {Advances in Databases and Information Systems},
   pages = {75-89},
   publisher = {Springer International Publishing},
   title = {Road Network Graph Representation for Traffic Analysis and Routing},
   year = {2022},
}
@article{Wei2021,
   abstract = {Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems},
   author = {Hua Wei and Guanjie Zheng and Vikash Gayah and Zhenhui Li},
   city = {New York, NY, USA},
   doi = {10.1145/3447556.3447565},
   issn = {1931-0145},
   issue = {2},
   journal = {SIGKDD Explor. Newsl.},
   month = {1},
   pages = {12-18},
   publisher = {Association for Computing Machinery},
   title = {Recent Advances in Reinforcement Learning for Traffic Signal Control: A Survey of Models and Evaluation},
   volume = {22},
   url = {https://doi.org/10.1145/3447556.3447565},
   year = {2021},
}
@article{Wu2020,
   author = {Wei Wu and Stephen John Elliott and Sen Lin and Shenshen Sun and Yandong Tang},
   issn = {2047-4938},
   issue = {1},
   journal = {IET Biometrics},
   pages = {1-10},
   publisher = {Wiley Online Library},
   title = {Review of palm vein recognition},
   volume = {9},
   year = {2020},
}
@article{Qi2021,
   author = {Delong Qi and Weijun Tan and Qi Yao and Jingfeng Liu},
   journal = {arXiv preprint arXiv:2105.12931},
   title = {YOLO5Face: why reinventing a face detector},
   year = {2021},
}
@article{Qader2023,
   author = {Sarchil Hama Qader and Chigozie Edson Utazi and Rhorom Priyatikanto and Peshawa Najmaddin and Emad Omer Hama-Ali and Nabaz Rasheed Khwarahm and Andrew J Tatem and Jadu Dash},
   issn = {0048-9697},
   journal = {Science of The Total Environment},
   pages = {161716},
   publisher = {Elsevier},
   title = {Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems},
   year = {2023},
}
@inproceedings{Bermejo2020,
   author = {Carlos Bermejo and Dimitris Chatzopoulos and Pan Hui},
   journal = {Proceedings of the 28th ACM International Conference on Multimedia},
   pages = {2765-2774},
   title = {Eyeshopper: Estimating shoppers' gaze using cctv cameras},
   year = {2020},
}

@inproceedings{Wang2020,
   author = {Xueyang Wang and Xiya Zhang and Yinheng Zhu and Yuchen Guo and Xiaoyun Yuan and Liuyu Xiang and Zerun Wang and Guiguang Ding and David Brady and Qionghai Dai},
   journal = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
   pages = {3268-3278},
   title = {Panda: A gigapixel-level human-centric video dataset},
   year = {2020},
}
@inproceedings{Kellnhofer2019,
   author = {Petr Kellnhofer and Adria Recasens and Simon Stent and Wojciech Matusik and Antonio Torralba},
   journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
   pages = {6912-6921},
   title = {Gaze360: Physically unconstrained gaze estimation in the wild},
   year = {2019},
}
@inproceedings{Dacayan2022,
   author = {T Dacayan and D Kwak and X Zhang},
   doi = {10.1109/PRAI55851.2022.9904097},
   journal = {2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)},
   pages = {533-538},
   title = {Computer-Vision Based Attention Monitoring for Online Meetings},
   year = {2022},
}
@article{Chatterjee2022,
   author = {Rajdeep Chatterjee and Rohit Halder and Tanmoy Maitra and Santosh Pani},
   issn = {1573-7721},
   journal = {Multimedia Tools and Applications},
   pages = {1-25},
   publisher = {Springer},
   title = {A computer vision-based perceived attention monitoring technique for smart teaching},
   year = {2022},
}
@article{Aljabarti2023,
   author = {Eman Attallah Aljabarti and Mohd Shahizan Othman},
   journal = {Available at SSRN 4320161},
   title = {Classification Students’ Attention in E-learning Using Machine Learning},
   year = {2023},
}
@inproceedings{Senarath2022,
   author = {Shashimal Senarath and Primesh Pathirana and Dulani Meedeniya and Sampath Jayarathna},
   isbn = {1665495014},
   journal = {2022 International Conference on Decision Aid Sciences and Applications (DASA)},
   pages = {1040-1044},
   publisher = {IEEE},
   title = {Retail gaze: A dataset for gaze estimation in retail environments},
   year = {2022},
}
@inproceedings{Touvron2021,
   author = {Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
   isbn = {2640-3498},
   journal = {International Conference on Machine Learning},
   pages = {10347-10357},
   publisher = {PMLR},
   title = {Training data-efficient image transformers & distillation through attention},
   year = {2021},
}
@inproceedings{Behera2020,
   author = {Ardhendu Behera and Zachary Wharton and Pradeep Hewage and Swagat Kumar},
   journal = {Proceedings of the Asian Conference on Computer Vision},
   title = {Rotation axis focused attention network (RAFA-Net) for estimating head pose},
   year = {2020},
}
@inproceedings{Ling2021,
   author = {Xufeng Ling and Dong Wang and Jie Yang},
   journal = {2021 7th International Conference on Computing and Artificial Intelligence},
   pages = {154-159},
   title = {A New Head Pose Estimation Method Using Vision Transformer Model},
   year = {2021},
}
@article{Zhou2023,
   author = {Ce Zhou and Qian Li and Chen Li and Jun Yu and Yixin Liu and Guangjing Wang and Kai Zhang and Cheng Ji and Qiben Yan and Lifang He},
   journal = {arXiv preprint arXiv:2302.09419},
   title = {A comprehensive survey on pretrained foundation models: A history from bert to chatgpt},
   year = {2023},
}


@inproceedings{Mazzeo2021,
   author = {Pier Luigi Mazzeo and Dilan D'Amico and Paolo Spagnolo and Cosimo Distante},
   isbn = {9532901124},
   journal = {2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)},
   pages = {1-6},
   publisher = {IEEE},
   title = {Deep learning based eye gaze estimation and prediction},
   year = {2021},
}


@misc{Authors14,
 author = {FirstName LastName},
 title = {The frobnicatable foo filter},
 note = {Face and Gesture submission ID 324. Supplied as supplemental material {\tt fg324.pdf}},
 year = 2014
}

@misc{Authors14b,
 author = {FirstName LastName},
 title = {Frobnication tutorial},
 note = {Supplied as supplemental material {\tt tr.pdf}},
 year = 2014
}

@article{Alpher02,
author = {FirstName Alpher},
title = {Frobnication},
journal = PAMI,
volume = 12,
number = 1,
pages = {234--778},
year = 2002
}

@article{Alpher03,
author = {FirstName Alpher and  FirstName Fotheringham-Smythe},
title = {Frobnication revisited},
journal = {Journal of Foo},
volume = 13,
number = 1,
pages = {234--778},
year = 2003
}

@article{Alpher04,
author = {FirstName Alpher and FirstName Fotheringham-Smythe and FirstName Gamow},
title = {Can a machine frobnicate?},
journal = {Journal of Foo},
volume = 14,
number = 1,
pages = {234--778},
year = 2004
}

@inproceedings{Alpher05,
author = {FirstName Alpher and FirstName Gamow},
title = {Can a computer frobnicate?},
booktitle = CVPR,
pages = {234--778},
year = 2005
}


@inproceedings{tomas2021goo,
  title={GOO: A Dataset for Gaze Object Prediction in Retail Environments},
  author={Tomas, Henri and Reyes, Marcus and Dionido, Raimarc and Ty, Mark and Casimiro, Joel and Atienza, Rowel and Guinto, Richard},
  booktitle = {CVPR Workshops (CVPRW)},
  year={2021},
  pubstate={published},
  tppubtype={inproceedings}
}


@inproceedings{10.5555/2968826.2968881,
author = {Zhou, Bolei and Lapedriza, Agata and Xiao, Jianxiong and Torralba, Antonio and Oliva, Aude},
title = {Learning Deep Features for Scene Recognition Using Places Database},
year = {2014},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
abstract = {Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks.},
booktitle = {Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1},
pages = {487–495},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS'14}
}

@inproceedings{eyediap,
author = {Funes Mora, Kenneth and Monay, Florent and Odobez, Jean-Marc},
year = {2014},
month = {03},
pages = {255-258},
title = {EYEDIAP: a database for the development and evaluation of gaze estimation algorithms from RGB and RGB-D cameras},
doi = {10.1145/2578153.2578190}
}

@INPROCEEDINGS{9577574,
  author={Fang, Yi and Tang, Jiapeng and Shen, Wang and Shen, Wei and Gu, Xiao and Song, Li and Zhai, Guangtao},
  booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Dual Attention Guided Gaze Target Detection in the Wild}, 
  year={2021},
  volume={},
  number={},
  pages={11385-11394},
  doi={10.1109/CVPR46437.2021.01123}}



@article{adam,
author = {Kingma, Diederik and Ba, Jimmy},
year = {2014},
month = {12},
pages = {},
title = {Adam: A Method for Stochastic Optimization},
journal = {International Conference on Learning Representations}
}

@InProceedings{10.1007/978-3-030-20893-6_3,
author="Lian, Dongze
and Yu, Zehao
and Gao, Shenghua",
editor="Jawahar, C. V.
and Li, Hongdong
and Mori, Greg
and Schindler, Konrad",
title="Believe It or Not, We Know What You Are Looking At!",
booktitle="Computer Vision -- ACCV 2018",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="35--50",
abstract="By borrowing the wisdom of human in gaze following, we propose a two-stage solution for gaze point prediction of the target persons in a scene. Specifically, in the first stage, both head image and its position are fed into a gaze direction pathway to predict the gaze direction, and then multi-scale gaze direction fields are generated to characterize the distribution of gaze points without considering the scene contents. In the second stage, the multi-scale gaze direction fields are concatenated with the image contents and fed into a heatmap pathway for heatmap regression. There are two merits for our two-stage solution based gaze following: (i) our solution mimics the behavior of human in gaze following, therefore it is more psychological plausible; (ii) besides using heatmap to supervise the output of our network, we can also leverage gaze direction to facilitate the training of gaze direction pathway, therefore our network can be more robustly trained. Considering that existing gaze following dataset is annotated by the third-view persons, we build a video gaze following dataset, where the ground truth is annotated by the observers in the videos. Therefore it is more reliable. The evaluation with such a dataset reflects the capacity of different methods in real scenarios better. Extensive experiments on both datasets show that our method significantly outperforms existing methods, which validates the effectiveness of our solution for gaze following. Our dataset and codes are released in https://github.com/svip-lab/GazeFollowing.",
isbn="978-3-030-20893-6"
}


@misc{zhao2017pyramid,
      title={Pyramid Scene Parsing Network}, 
      author={Hengshuang Zhao and Jianping Shi and Xiaojuan Qi and Xiaogang Wang and Jiaya Jia},
      year={2017},
      eprint={1612.01105},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}


@article{ILSVRC15,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = {{ImageNet Large Scale Visual Recognition Challenge}},
Year = {2015},
journal   = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}

@article{10.1016/j.patcog.2014.01.005,
author = {Garrido-Jurado, S. and Mu\~{n}oz-Salinas, R. and Madrid-Cuevas, F.J. and Mar\'{\i}n-Jim\'{e}nez, M.J.},
title = {Automatic Generation and Detection of Highly Reliable Fiducial Markers under Occlusion},
year = {2014},
issue_date = {June, 2014},
publisher = {Elsevier Science Inc.},
address = {USA},
volume = {47},
number = {6},
issn = {0031-3203},
url = {https://doi.org/10.1016/j.patcog.2014.01.005},
doi = {10.1016/j.patcog.2014.01.005},
abstract = {This paper presents a fiducial marker system specially appropriated for camera pose estimation in applications such as augmented reality and robot localization. Three main contributions are presented. First, we propose an algorithm for generating configurable marker dictionaries (in size and number of bits) following a criterion to maximize the inter-marker distance and the number of bit transitions. In the process, we derive the maximum theoretical inter-marker distance that dictionaries of square binary markers can have. Second, a method for automatically detecting the markers and correcting possible errors is proposed. Third, a solution to the occlusion problem in augmented reality applications is shown. To that aim, multiple markers are combined with an occlusion mask calculated by color segmentation. The experiments conducted show that our proposal obtains dictionaries with higher inter-marker distances and lower false negative rates than state-of-the-art systems, and provides an effective solution to the occlusion problem. HighlightsWe propose an algorithm for generating configurable marker dictionaries.We derive the maximum theoretical inter-marker distance.A method for automatically detecting the markers and correcting errors is proposed.A solution to the occlusion problem in augmented reality applications is shown.},
journal = {Pattern Recogn.},
month = {jun},
pages = {2280–2292},
numpages = {13},
keywords = {Fiducial marker, Augmented reality, Computer vision}
}



@misc{lin2017feature,
      title={Feature Pyramid Networks for Object Detection}, 
      author={Tsung-Yi Lin and Piotr Dollár and Ross Girshick and Kaiming He and Bharath Hariharan and Serge Belongie},
      year={2017},
      eprint={1612.03144},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@INPROCEEDINGS{9878884,
  author={Bao, Jun and Liu, Buyu and Yu, Jun},
  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={ESCNet: Gaze Target Detection with the Understanding of 3D Scenes}, 
  year={2022},
  volume={},
  number={},
  pages={14106-14115},
  doi={10.1109/CVPR52688.2022.01373}}


@article{OmerSumer2021multimodalengagement,
   author = {Ömer Sümer and Patricia Goldberg and Sidney D'Mello and Peter Gerjets and Ulrich Trautwein and Enkelejda Kasneci},
   journal = {arXiv preprint arXiv:2101.04215},
   title = {Multimodal engagement analysis from facial videos in the classroom},
   year = {2021},
}
@inproceedings{Senarath2022RetailEnvironments,
    title = {{Retail gaze: A dataset for gaze estimation in retail environments}},
    year = {2022},
    booktitle = {2022 International Conference on Decision Aid Sciences and Applications (DASA)},
    author = {Senarath, Shashimal and Pathirana, Primesh and Meedeniya, Dulani and Jayarathna, Sampath},
    pages = {1040--1044},
    publisher = {IEEE},
    isbn = {1665495014}
}
@inproceedings{Tonini_2022,
	doi = {10.1145/3536221.3556624},  
	url ={https://doi.org/10.1145%2F3536221.3556624},
  
	year = 2022,
	month = {nov},
  
	publisher = {{ACM}},
  
	author = {Francesco Tonini and Cigdem Beyan and Elisa Ricci},
  
	title = {Multimodal Across Domains Gaze Target Detection},
  
	booktitle = {{International} {Conference} {on} {Multimodal} {Interaction}}
}

@misc{ranftl2020robust,
      title={Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer}, 
      author={René Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun},
      year={2020},
      eprint={1907.01341},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{ranftl2021vision,
      title={Vision Transformers for Dense Prediction}, 
      author={René Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
      year={2021},
      eprint={2103.13413},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{he2015deep,
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      archivePrefix={arXiv},
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}
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}

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  year={2020},
  publisher={IEEE}
}

@inproceedings{cvpr2016_gazecapture,
Author = {Kyle Krafka and Aditya Khosla and Petr Kellnhofer and Harini Kannan and Suchendra Bhandarkar and Wojciech Matusik and Antonio Torralba},
Title = {Eye Tracking for Everyone},
Year = {2016},
Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}
}

@inproceedings{thakur2021predicting,
  title={Predicting gaze from egocentric social interaction videos and imu data},
  author={Thakur, Sanket Kumar and Beyan, Cigdem and Morerio, Pietro and Del Bue, Alessio},
  booktitle={Proceedings of the 2021 International Conference on Multimodal Interaction},
  pages={717--722},
  year={2021}
}
@inproceedings{zhang2018training,
  title={Training person-specific gaze estimators from user interactions with multiple devices},
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  pages={113--126},
  year={2015},
  publisher={Elsevier}
}

@inproceedings{zhang2015appearance,
  title={Appearance-based gaze estimation in the wild},
  author={Zhang, Xucong and Sugano, Yusuke and Fritz, Mario and Bulling, Andreas},
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  pages={4511--4520},
  year={2015}
}
@inproceedings{connecting_gaze_scene_attention,
author = {Chong, Eunji and Ruiz, Nataniel and Wang, Yongxin and Zhang, Yun and Rozga, Agata and Rehg, James M.},
title = {Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency},
year = {2018},
isbn = {978-3-030-01227-4},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-030-01228-1_24},
doi = {10.1007/978-3-030-01228-1_24},
abstract = {.},
booktitle = {Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part V},
pages = {397–412},
numpages = {16},
keywords = {Visual attention, Gaze estimation, Saliency},
location = {Munich, Germany}
}

@article{Guo2023LiteGaze:Estimation,
    title = {{LiteGaze: Neural architecture search for efficient gaze estimation}},
    year = {2023},
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    number = {5},
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    publisher = {Public Library of Science},
    url = {https://doi.org/10.1371/journal.pone.0284814}
}
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    title = {{Classroom Attention Estimation Method Based on Mining Facial Landmarks of Students}},
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    pages = {255--266},
    publisher = {Springer}
}


@article{Mukherjee2015DeepVideo,
    title = {{Deep head pose: Gaze-direction estimation in multimodal video}},
    year = {2015},
    journal = {IEEE Transactions on Multimedia},
    author = {Mukherjee, Sankha S and Robertson, Neil Martin},
    number = {11},
    pages = {2094--2107},
    volume = {17},
    publisher = {IEEE},
    issn = {1520-9210}
}
@article{JIN2022104924,
title = {Depth-aware gaze-following via auxiliary networks for robotics},
journal = {Engineering Applications of Artificial Intelligence},
volume = {113},
pages = {104924},
year = {2022},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2022.104924},
url = {https://www.sciencedirect.com/science/article/pii/S0952197622001464},
author = {Tianlei Jin and Qizhi Yu and Shiqiang Zhu and Zheyuan Lin and Jie Ren and Yuanhai Zhou and Wei Song},
keywords = {Gaze-following, Depth-aware, Human–robot interaction},
abstract = {Gaze-Following aims to predict the gaze target of a subject within an image, and information on orientation and depth greatly improves this task. However, previous methods require additional datasets to obtain depth or orientation information, leading to cumbersome training or inference processes. To this end, we propose an end-to-end depth-aware gaze-following approach that incorporates depth and orientation information without additional datasets. Our approach identifies a primary task, gaze-following, supervised by true labels from the gaze-following dataset and two auxiliary tasks, scene depth estimation and 3D orientation estimation, supervised by generated pseudo labels. Intermediate auxiliary features are integrated into the primary task network as implicit information. We propose a residual filter module for screening useful information that can enhance gaze-following prediction performance. Extensive experiments on GazeFollow and VideoAttentionTarget show that our approach achieves state-of-the-art results (0.120 Ave. Dist. achieved on GazeFollow and 0.104 L2 Dist. achieved on VideoAttentionTarget). Finally, we apply our approach to a real robot for understanding human attention and intention. Compared to the previous depth considered gaze-following method, our method saves half of the computation time.}
}