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Staged encoder training for cross-camera person re-identification

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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.

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Data availability

The datasets are available at https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/data.zip. Code is available at https://github.com/-yjwyuanwu/SET.

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Funding

This work was supported by Guangxi Natural Science Foundation (No. 2020GXNSFAA297186), Jiangsu Province Agricultural Science and Technology Innovation and Promotion Special Project (No. NJ2021-21), Guilin Key Research and Development Program (No. 20210206-1), Guan-gxi Key Laboratory of Precision Navigation Technology and Application (No. DH202227), Guangxi Key Laboratory of Image and Graphic Intelligent Processing (No. GIIP2301). There are no financial conflicts of interest to disclose.

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Authors and Affiliations

  1. School of Computer Information and Security, Guilin University of Electronic Technology, Guilin, China

    Zhi Xu, Jiawei Yang & Longyang Zhao

  2. School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, China

    Yuxuan Liu

  3. School of Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan, China

    Jiajia Liu

Authors
  1. Zhi Xu
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  2. Jiawei Yang
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  3. Yuxuan Liu
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  4. Longyang Zhao
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  5. Jiajia Liu
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Contributions

ZX contributed to conceptualization, methodology, software, resources, writing—review and editing. JY contributed to methodology, software, validation and writing—original draft. YL contributed to supervision and writing—original draft. LZ contributed to Data curation and Investigation. JL contributed to formal analysis and Data curation. All authors reviewed the manuscript.

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Correspondence to Zhi Xu.

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Xu, Z., Yang, J., Liu, Y. et al. Staged encoder training for cross-camera person re-identification. SIViP 18, 2323–2331 (2024). https://doi.org/10.1007/s11760-023-02909-0

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  • DOI: https://doi.org/10.1007/s11760-023-02909-0

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