Overview of deep learning based pedestrian attribute recognition and re-identification

Pedestrian attribute recognition (PAR) and re-identification (ReID) are important works in the area of computer vision, which are widely used in intelligent surveillance and are of great significance to the creation of smart life. The purpose of this article is to focus on organizing a review of ReID based on deep learning and analyze the associations between PAR and ReID. Firstly, we summarize the major ideas of Attribute-Assisted ReID and compare the differences in datasets and algorithmic concerns between the two areas. Secondly, we introduce a wide range of representative ReID methods. By analyzing some cutting-edge researches, we summarize their specific network structure, loss function design, and effective training tricks. Reference methods and solutions are provided for the main challenges of ReID, such as cloth-changing, domain adaptation, occlusion condition, resolution changes, etc. Finally, we conclude the performance and characteristics of the SOTA methods, obtain inspiration and prospects for future research directions, and demonstrate the effectiveness of Attribute-Assisted ReID.

Automated summary

This article provides an overview of the importance of pedestrian attribute recognition (PAR) and re-identification (ReID) in the field of computer vision. It focuses on ReID based on deep learning and analyzes the associations between PAR and ReID. The article summarizes the major ideas and methods of Attribute-Assisted ReID, and provides solutions for addressing challenges in ReID. It concludes the performance and characteristics of state-of-the-art methods, presents future research directions, and demonstrates the effectiveness of Attribute-Assisted ReID.