Multi-target tracking for video surveillance using deep affinity network: a brief review

TL;DR

This paper reviews deep learning-based multi-target tracking models for video surveillance, highlighting their capabilities and challenges in object detection, trajectory estimation, and re-identification.

Contribution

It provides a comprehensive overview of the latest deep learning approaches in multi-target tracking, emphasizing their potential and limitations.

Findings

Deep learning models improve object tracking accuracy.

Handling occlusions remains a significant challenge.

Integration of detection, tracking, and re-identification enhances performance.

Abstract

Deep learning models are known to function like the human brain. Due to their functional mechanism, they are frequently utilized to accomplish tasks that require human intelligence. Multi-target tracking (MTT) for video surveillance is one of the important and challenging tasks, which has attracted the researcher's attention due to its potential applications in various domains. Multi-target tracking tasks require locating the objects individually in each frame, which remains a huge challenge as there are immediate changes in appearances and extreme occlusions of objects. In addition to that, the Multitarget tracking framework requires multiple tasks to perform i.e. target detection, estimating trajectory, associations between frame, and re-identification. Various methods have been suggested, and some assumptions are made to constrain the problem in the context of a particular problem. In this paper, the state-of-the-art MTT models, which leverage from deep learning representational power are reviewed.