# Deep Learning in Video Multi-Object Tracking: A Survey

## TL;DR

This survey reviews how deep learning techniques have been applied to multi-object tracking in videos, analyzing methods across different stages and comparing their performance on standard datasets.

## Contribution

It provides a comprehensive overview of deep learning applications in MOT, including a detailed experimental comparison and insights into future research directions.

## Key findings

- Deep learning improves MOT accuracy and robustness.
- Top methods share common architectural features.
- Future research should focus on real-time processing and occlusion handling.

## 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.