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

Yan Huang, Yan Huang, Zhang Zhang, Qiang Wu, Yi Zhong, Liang Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalInternational Journal of Computer Vision
DOIs
Publication statusAccepted/In press - 2025

Keywords

  • Clothing status awareness
  • Feature regularization
  • Long-term person re-identification

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Huang, Y., Huang, Y., Zhang, Z., Wu, Q., Zhong, Y., & Wang, L. (Accepted/In press). CSFRNet: Integrating Clothing Status Awareness for Long-Term Person Re-identification. International Journal of Computer Vision. https://doi.org/10.1007/s11263-024-02315-0