A survey on person and vehicle re‐identification

• Published in: IET computer vision Vol. 18; no. 8; pp. 1235 - 1268
• Main Authors: Wang, Zhaofa, Wang, Liyang, Shi, Zhiping, Zhang, Miaomiao, Geng, Qichuan, Jiang, Na
• Format: Journal Article
• Language: English
• Published: Stevenage John Wiley & Sons, Inc 01.12.2024; Wiley
• Subjects: Cameras; Computer vision; Datasets; Deep learning; Dictionaries; Identification; image retrieval; Neural networks; Retrieval; Surveillance; Vehicles
• ISSN: 1751-9632; 1751-9640
• DOI: 10.1049/cvi2.12316

Person/vehicle re‐identification aims to use technologies such as cross‐camera retrieval to associate the same person (same vehicle) in the surveillance videos at different locations, different times, and images captured by different cameras so as to achieve cross‐surveillance image matching, person retrieval and trajectory tracking. It plays an extremely important role in the fields of intelligent security, criminal investigation etc. In recent years, the rapid development of deep learning technology has significantly propelled the advancement of re‐identification (Re‐ID) technology. An increasing number of technical methods have emerged, aiming to enhance Re‐ID performance. This paper summarises four popular research areas in the current field of re‐identification, focusing on the current research hotspots. These areas include the multi‐task learning domain, the generalisation learning domain, the cross‐modality domain, and the optimisation learning domain. Specifically, the paper analyses various challenges faced within these domains and elaborates on different deep learning frameworks and networks that address these challenges. A comparative analysis of re‐identification tasks from various classification perspectives is provided, introducing mainstream research directions and current achievements. Finally, insights into future development trends are presented. This paper summarises four popular research areas in the current field of re‐identification, focusing on the current research hotspots. These areas include the multi‐task learning domain, the generalisation learning domain, the cross‐modality domain, and the optimisation learning domain. Specifically, the paper analyses the various challenges faced within these domains and elaborates on different deep learning frameworks and networks that address these challenges. A comparative analysis of re‐identification tasks from various classification perspectives is provided, introducing mainstream research directions and current achievements. Finally, insights into future development trends are presented.