A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework

• Published in: IEEE transactions on circuits and systems for video technology Vol. 31; no. 5; pp. 1819 - 1833
• Main Authors: Sun, Zhihong, Chen, Jun, Chao, Liang, Ruan, Weijian, Mukherjee, Mithun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer vision; data association; Machine learning; multiple pedestrian tracking; Noise measurement; Radar tracking; Task analysis; Tracking; tracking-by-detection; Trajectory; Video sequences; Visualization
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2020.3009717

Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection algorithms, Tracking-by-Detection (TBD) based algorithms have achieved tremendous successes. TBD has become the most popular MPT framework, and it has been actively studied in the past decade. In this paper, we give a comprehensive survey of recent advances in TBD-based MPT algorithms. We systematically analyze the existing TBD-based algorithms and organize the survey into four major parts. At first, this survey draws a timeline to introduce the milestones of TBD-based works which briefly reviews the development of the existing TBD-based methods. Second, the main procedures of the TBD framework are summarized, and each stage in the procedure is described in detail. Afterward, this survey analyzes the performance of existing TBD-based algorithms on MOT challenge datasets and discusses the factors that affect tracking performance. Finally, open issues and future directions in the TBD framework are discussed.