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

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 field of computer vision. Its main research goal 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 costs. With the widespread application of deep neural networks in the field of computer vision, a large number of deep learning-based person Re-ID methods have emerged. To facilitate researchers to better understand the latest research results and future development trends in this field. Firstly, we compare traditional and deep learning-based person Re-ID methods, and present the main contributions of several person Re-ID surveys, and analyze their focused dimensions and shortcomings. Secondly, we focus on the current classic deep learning-based person Re-ID methods, including methods for deep metric learning, local feature learning, generate adversarial networks, sequence feature learning, and graph convolutional networks. Furthermore, we subdivide the above five categories according to their technique types, analyzing and comparing the experimental performance of part subcategories of the method. Finally, we discuss the challenges that remain in the field of person Re-ID field and prospects for future research directions.