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.

本文综述了利用深度学习模型解决单摄像头视频的多目标跟踪任务的研究，总结了该任务中的四个主要步骤，并深入探讨了如何在每个步骤中利用深度学习。此外，还提供了对三个 MOTChallenge 数据集中所呈现的工作的完整实验比较，并确定了最优解方法之间的若干相似之处，提出了一些可能的未来研究方向。