Deep Learning in Video Multi-Object Tracking: A Survey
Abstract
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.
Cite
@article{arxiv.1907.12740,
title = {Deep Learning in Video Multi-Object Tracking: A Survey},
author = {Gioele Ciaparrone and Francisco Luque Sánchez and Siham Tabik and Luigi Troiano and Roberto Tagliaferri and Francisco Herrera},
journal= {arXiv preprint arXiv:1907.12740},
year = {2019}
}
Comments
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