Deep learning-based person re-identification methods: A survey and outlook of recent works

TL;DR

This survey reviews recent deep learning methods for person re-identification, classifying them into four categories, and discusses challenges and future research directions in the field.

Contribution

It provides a systematic classification of deep learning-based person Re-ID methods and offers insights into their advantages, limitations, and future trends.

Findings

Classifies deep learning-based Re-ID methods into four categories

Analyzes methodologies and motivations of different Re-ID approaches

Discusses challenges and future research directions

Abstract

In recent years, with the increasing demand for public safety and the rapid development of intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot research topics in the computer vision field. The main research goal of person Re-ID is to retrieve persons with the same identity from different cameras. However, traditional person Re-ID methods require manual marking of person targets, which consumes a lot of labor cost. With the widespread application of deep neural networks, many deep learning-based person Re-ID methods have emerged. Therefore, this paper is to facilitate researchers to understand the latest research results and the future trends in the field. Firstly, we summarize the studies of several recently published person Re-ID surveys and complement the latest research methods to systematically classify deep learning-based person Re-ID methods. Secondly, we propose a multi-dimensional taxonomy that classifies current deep learning-based person Re-ID methods into four categories according to metric and representation learning, including methods for deep metric learning, local feature learning, generative adversarial learning and sequence feature learning. Furthermore, we subdivide the above four categories according to their methodologies and motivations, discussing the advantages and limitations of part subcategories. Finally, we discuss some challenges and possible research directions for person Re-ID.