Deep learning in person re-identification: a survey

Zhigang Xiao; Zhaoguo Zhang; Yi Ning; Yi Lu; Wei Song; Xingguo Qing; Jinlong Chen

doi:10.1117/12.3035149

19 July 2024 Deep learning in person re-identification: a survey

Zhigang Xiao, Zhaoguo Zhang, Yi Ning, Yi Lu, Wei Song, Xingguo Qing, Jinlong Chen

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133T (2024) https://doi.org/10.1117/12.3035149
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

In the era of advancing deep learning, person re-identification has gained widespread application in domains such as video and security monitoring. Person re-identification seeks to recognize and match target persons across images taken by diverse cameras, thereby verifying whether the pedestrian subjects observed by cameras at various locations and times are indeed the same individual. This article categorizes current research into two main types: image-based person reidentification and video-based person re-identification. It provides a thorough overview and analysis of existing literature concerning classification methods, verification models, attention mechanisms, and metric learning, all centered around the research subjects. Additionally, it outlines the progression of datasets and provides insights into the anticipated future trends in person re-identification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhigang Xiao, Zhaoguo Zhang, Yi Ning, Yi Lu, Wei Song, Xingguo Qing, and Jinlong Chen "Deep learning in person re-identification: a survey", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133T (19 July 2024); https://doi.org/10.1117/12.3035149

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KEYWORDS

Video

Video surveillance

Cameras

Deep learning

Feature extraction

Education and training

Statistical modeling

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