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CSFRNet: Integrating Clothing Status Awareness for Long-Term Person Re-identification

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Abstract

Addressing the dynamic nature of long-term person re-identification (LT-reID) amid varying clothing conditions necessitates a departure from conventional methods. Traditional LT-reID strategies, mainly biometrics-based and data adaptation-based, each have their pitfalls. The former falters in environments lacking high-quality biometric data, while the latter loses efficacy with minimal or subtle clothing changes. To overcome these obstacles, we propose the clothing status-aware feature regularization network (CSFRNet). This novel approach seamlessly incorporates clothing status awareness into the feature learning process, significantly enhancing the adaptability and accuracy of LT-reID systems where clothing can either change completely, partially, or not at all over time, without the need for explicit clothing labels. The versatility of our CSFRNet is showcased on diverse LT-reID benchmarks, including Celeb-reID, Celeb-reID-light, PRCC, DeepChange, and LTCC, marking a significant advancement in the field by addressing the real-world variability of clothing in LT-reID scenarios.

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Acknowledgements

This work was jointly supported by National Natural Science Foundation of China (Grant Nos. 62306311, 62373355, 62236010, 62276261, and 62201061), National Key R and D Program of China (Grant No. 2022ZD0117901), Key Research Program of Frontier Sciences CAS, China (Grant No. ZDBSLYJSC032), and the ARC with Grant No. DP230101540.

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Authors and Affiliations

  1. New Laboratory of Pattern Recognition (NLPR), State key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yan Huang, Yan Huang, Zhang Zhang & Liang Wang

  2. The School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS), Beijing, China

    Zhang Zhang & Liang Wang

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Yan Huang & Qiang Wu

  4. The School of Information and Electronics, Beijing Institute of Technology, Beijing, China

    Yi Zhong

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  1. Yan Huang
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Correspondence to Liang Wang.

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Communicated by Shaogang Gong.

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Huang, Y., Huang, Y., Zhang, Z. et al. CSFRNet: Integrating Clothing Status Awareness for Long-Term Person Re-identification. Int J Comput Vis 133, 3180–3202 (2025). https://doi.org/10.1007/s11263-024-02315-0

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