A Survey of Person Re-identification Based on Deep Learning
Authors: 
Zimin Tian, Si Chen, Da-Han Wang, Junwen LuAuthors Info & Claims
Pages 36 - 42
Abstract
Person re-identification (Re-ID) has been a popular research topic in computer vision in recent years, and it has important application value in numerous fields, such as intelligent security. The person Re-ID task is to identify whether the pedestrians appearing under different cameras are the same person. The traditional person Re-ID methods mainly rely on the characteristics of manual design, and it has difficulty in solving the problems of person occlusion, posture change, and illumination variation. With the wide application of deep learning, the person Re-ID based on deep learning has brought new ideas for solving these problems, and has been widely concerned by scholars. This paper summarizes and analyzes the latest research trends of person Re-ID based on deep learning. In our work, the recent research works of person Re-ID are coarsely categorized into the supervised learning methods and the unsupervised learning methods according to whether the pedestrian images in the training set have real labels. We then describe the representative datasets used in the person Re-ID task. Finally, we conclude and discuss the future directions of the person Re-ID based on deep learning.
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Published In
October 2021
393 pages
ISBN:9781450390439
DOI:10.1145/3497623
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Published: 04 February 2022
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- Natural Science Foundation of Fujian Province
- Young Teacher Education Research Project of Fujian Province
- Natural Science Foundation of China
- Youth Innovation Fund Project of Xiamen City
- Scientific Research Climbing Program of Xiamen University of Technology
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ICCPR '21
ICCPR '21: 2021 10th International Conference on Computing and Pattern Recognition
October 15 - 17, 2021
Shanghai, China
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