Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230600138-7.doi: 10.11896/jsjkx.230600138

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Single Stage Unsupervised Visible-infrared Person Re-identification

LOU Ren1, HE Renqiang2, ZHAO Sanyuan2,3, HAO Xin2, ZHOU Yueqi1, WANG Xinyuan1, LI Fangfang4   

  1. 1 Zhejiang Academy of Transportation Sciences,Hangzhou 310000,China
    2 School of Computer Science,Beijing Institute of Technology,Beijing 100081,China
    3 Yangtze River Delta Research Institute(Jiaxing),Beijing Institute of Technology,Jiaxing,Zhejiang 314011,China
    4 Enterprise Institute of Zhejiang Communications Investment Expressway Operation Management Co.Ltd,Hangzhou 310000,China
  • Published:2024-06-06
  • About author:LOU Ren,born in 1982,bachelor,senior engineer.His main research interests include transportation Internet of Things and artificial intelligence.
    ZHAO Sanyuan,born in 1985,Ph.D,associate professor.Her main research interests include computer vision,deep learning and virtual reality.
  • Supported by:
    Research on Evaluation Technology of Traffic Flow based on Vision-Radar Fusion Perception System(202209) and Research on Vehicle Trajectory Data Quality Evaluation Technology Based on Radar-vision Integrated Equipment(LGC22E080003).

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Abstract: The unsupervised visible-infrared multi-modal person re-identification can alleviate the problem that a lot of manual labeling is required in the intelligent monitoring scene.Common multi-stage models are used to process different modal data separately.This paper proposes an effective single-stage unsupervised cross-modal pedestrian recognition method,and designs a clustering algorithm based on confidence factor and a cross-modal feature processing method based on graph embedding to solve the unlabeled problem and cross-modal problem respectively.Experimental results show that compared with the existing algorithms,the proposed algorithm has achieved an improvement of at least 7% in the case of r=1.

Key words: Cross-modal learning, Unsupervised person re-identification, Visible-infrared person re-identification, Unsupervised learning, Cross-modal feature processing

CLC Number: 

  • TP391
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