Deep learning in video multi-object tracking: A survey
Abstrak
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.
Topik & Kata Kunci
Penulis (6)
G. Ciaparrone
Francisco Luque Sánchez
S. Tabik
L. Troiano
R. Tagliaferri
Francisco Herrera
Akses Cepat
- Tahun Terbit
- 2019
- Bahasa
- en
- Total Sitasi
- 647×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1016/j.neucom.2019.11.023
- Akses
- Open Access ✓