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Multiple Object Tracking: A Literature Review
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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.
Forward citations
Cited by 3 Pith papers
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Multi Target Tracking by Learning from Generalized Graph Differences
A training scheme that learns network-flow tracker weights from small perturbations of ground-truth tracks, represented as generalized graph differences, achieves competitive DukeMTMCT MOTA without solver-in-the-loop ...
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Multiple Object Tracking with Motion and Appearance Cues
Adding optical-flow motion compensation, appearance-based cascade matching, and an auxiliary predictor to an IoU tracker improves multiple object tracking on VisDrone, with reported MOTA rising from 12.6 to 32.1.
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Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning
A HISP filter tracker with deep appearance features (HISP-DAL) reaches 37.4 MOTA on MOT16 and 45.4 MOTA on MOT17 using public detections.
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