Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook

Sajid Javed

Abstract:

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It
entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough
approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have
emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object
tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based
on results in nine tracking benchmarks. First, we present the background theory of both the DCF and Siamese tracking core
formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these
tracking paradigms. Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering
different experimental aspects of visual tracking: datasets, evaluation metrics, performance, and speed comparisons. We finish the
survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis.
Index Terms—Visual Object Tracking, Discriminative Correlation Filters, Siamese Networks.