Staged encoder training for cross-camera person re-identification

As a cross-camera retrieval problem, person re-identification (ReID) suffers from image style variations caused by camera parameters, lighting and other reasons, which will seriously affect the model recognition accuracy. To address this problem, this paper proposes a two-stage contrastive learning...

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Published in:	Signal, image and video processing Vol. 18; no. 3; pp. 2323 - 2331
Main Authors:	Xu, Zhi, Yang, Jiawei, Liu, Yuxuan, Zhao, Longyang, Liu, Jiajia
Format:	Journal Article
Language:	English
Published:	London Springer London 01.04.2024 Springer Nature B.V
Subjects:	Accuracy Cameras Classification Coders Computer Imaging Computer Science Computer vision Datasets Dictionaries Image coding Image Processing and Computer Vision Methods Multimedia Information Systems Original Paper Parameter identification Pattern recognition Pattern Recognition and Graphics Signal,Image and Speech Processing Vision Unsupervised Person re-identification Camera variation Contrastive learning
ISSN:	1863-1703, 1863-1711
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Abstract	As a cross-camera retrieval problem, person re-identification (ReID) suffers from image style variations caused by camera parameters, lighting and other reasons, which will seriously affect the model recognition accuracy. To address this problem, this paper proposes a two-stage contrastive learning method to gradually reduce the impact of camera variations. In the first stage, we train an encoder for each camera using only images from the respective camera. This ensures that each encoder has better recognition performance on images from its respective camera while being unaffected by camera variations. In the second stage, we encode the same image using all trained encoders to generate a new combination code that is robust against camera variations. We also use Cross-Camera Encouragement (Lin et al., in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020) distance that complements the advantages of combined encoding to further mitigate the impact of camera variations. Our method achieves high accuracy on several commonly used person ReID datasets, e.g., on the Market-1501, achieves 90.8% rank-1 accuracy and 85.2% mAP, outperforming the recent unsupervised works by 12+% in terms of mAP. Code is available at https://github.com/yjwyuanwu/SET.
AbstractList	As a cross-camera retrieval problem, person re-identification (ReID) suffers from image style variations caused by camera parameters, lighting and other reasons, which will seriously affect the model recognition accuracy. To address this problem, this paper proposes a two-stage contrastive learning method to gradually reduce the impact of camera variations. In the first stage, we train an encoder for each camera using only images from the respective camera. This ensures that each encoder has better recognition performance on images from its respective camera while being unaffected by camera variations. In the second stage, we encode the same image using all trained encoders to generate a new combination code that is robust against camera variations. We also use Cross-Camera Encouragement (Lin et al., in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020) distance that complements the advantages of combined encoding to further mitigate the impact of camera variations. Our method achieves high accuracy on several commonly used person ReID datasets, e.g., on the Market-1501, achieves 90.8% rank-1 accuracy and 85.2% mAP, outperforming the recent unsupervised works by 12+% in terms of mAP. Code is available at https://github.com/yjwyuanwu/SET. As a cross-camera retrieval problem, person re-identification (ReID) suffers from image style variations caused by camera parameters, lighting and other reasons, which will seriously affect the model recognition accuracy. To address this problem, this paper proposes a two-stage contrastive learning method to gradually reduce the impact of camera variations. In the first stage, we train an encoder for each camera using only images from the respective camera. This ensures that each encoder has better recognition performance on images from its respective camera while being unaffected by camera variations. In the second stage, we encode the same image using all trained encoders to generate a new combination code that is robust against camera variations. We also use Cross-Camera Encouragement (Lin et al., in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020) distance that complements the advantages of combined encoding to further mitigate the impact of camera variations. Our method achieves high accuracy on several commonly used person ReID datasets, e.g., on the Market-1501, achieves 90.8% rank-1 accuracy and 85.2% mAP, outperforming the recent unsupervised works by 12+% in terms of mAP. Code is available at https://github.com/yjwyuanwu/SET.
Author	Liu, Yuxuan Xu, Zhi Zhao, Longyang Yang, Jiawei Liu, Jiajia
Author_xml	– sequence: 1 givenname: Zhi surname: Xu fullname: Xu, Zhi email: xuzhi@guet.edu.cn organization: School of Computer Information and Security, Guilin University of Electronic Technology – sequence: 2 givenname: Jiawei surname: Yang fullname: Yang, Jiawei organization: School of Computer Information and Security, Guilin University of Electronic Technology – sequence: 3 givenname: Yuxuan surname: Liu fullname: Liu, Yuxuan organization: School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology – sequence: 4 givenname: Longyang surname: Zhao fullname: Zhao, Longyang organization: School of Computer Information and Security, Guilin University of Electronic Technology – sequence: 5 givenname: Jiajia surname: Liu fullname: Liu, Jiajia organization: School of Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China
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Cites_doi	10.1109/TPAMI.2017.2666805 10.1016/j.cviu.2007.01.003 10.1109/TPAMI.2021.3054775 10.1109/CVPR42600.2020.00345 10.1109/CVPR.2018.00016 10.1109/CVPR.2018.00541 10.1109/CVPR42600.2020.01367 10.1109/CVPR.2018.00110 10.1609/aaai.v33i01.33018738 10.1109/ICCV.2015.133 10.1109/ICCV.2019.00801 10.1109/ICIP.2003.1246634 10.1109/CVPR46437.2021.01175 10.1007/978-3-030-01261-8_11 10.1109/CVPR.2019.00070 10.1007/978-3-030-58536-5_6 10.1109/CVPR.2009.5206848 10.1007/978-3-030-58621-8_31 10.1007/978-3-319-48881-3_2 10.1109/CVPR.2016.90 10.1109/CVPR42600.2020.01099 10.1109/CVPR.2018.00454 10.1109/CVPR.2019.00224
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SubjectTerms	Accuracy Cameras Classification Coders Computer Imaging Computer Science Computer vision Datasets Dictionaries Image coding Image Processing and Computer Vision Methods Multimedia Information Systems Original Paper Parameter identification Pattern recognition Pattern Recognition and Graphics Signal,Image and Speech Processing Vision
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Title	Staged encoder training for cross-camera person re-identification
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