Pedestrian attribute recognition: A survey

• Published in: Pattern recognition Vol. 121; p. 108220
• Main Authors: Wang, Xiao, Zheng, Shaofei, Yang, Rui, Zheng, Aihua, Chen, Zhe, Tang, Jin, Luo, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2022
• Subjects: CNN-RNN; Deep learning; Multi-label learning; Multi-task learning; Pedestrian attribute recognition; Pedestrian attribute recognition; Deep learning; Multi-label learning; CNN-RNN; Multi-task learning
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2021.108220

•The first survey paper for pedestrian attributes recognition (PAR).•Give a brief introduction to existing pedestrian attributes recognition algorithms.•Give various research directions for PAR. Pedestrian Attribute Recognition (PAR) is an important task in computer vision community and plays an important role in practical video surveillance. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attribute recognition, including the fundamental concepts and formulation of pedestrian attributes and corresponding challenges. Secondly, we analyze popular solutions for this task from eight perspectives. Thirdly, we discuss the specific attribute recognition, then, give a comparison between deep learning and traditional algorithm based PAR methods. After that, we show the connections between PAR and other computer vision tasks. Fourthly, we introduce the benchmark datasets, evaluation metrics in this community, and give a brief performance comparison. Finally, we summarize this paper and give several possible research directions for PAR. The project page of this paper can be found at: https://sites.google.com/view/ahu-pedestrianattributes/.