2019/07/31 by Gioele Ciaparrone, Francisco Luque Sánchez, Siham Tabik +3 · 1 citation
Computer Science · Mathematics · #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.1016/j.neucom.2019.11.023
Accepted in Neurocomputing, 2019. New in v4: updated license in compliance with Elsevier policy. Main text: 29 pages, 10 figures, 7 tables. Summary table in appendix at the end of the paper
arxiv created 2019/11/19 · arxiv updated 2019/11/21
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.