  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.
