基于位置掩码引导的换装行人重识别模型

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中图分类号：TP391文献标识码：A

Cloth-changing Person Re-identification Model Based on Positional Mask-guided

GE Jiashang，SONG Shimiao，GU Feifan，YANG Jie （College of Mechanical and Electrical Engineering，Qingdao University，Qingdao ，China）

Abstract： In cloth-changing person re-identification tasks，clothing variation is a critical factor degrading recognition accuracy. To discover clothing-invariant features，a Positional Mask-Guided Model （PMGM） for Cloth-changing Person Re-identification was proposed. The PMGM model leverages four positional masks （head，upper body，lower body，and arms） to guide the network in capturing local fine-grained features，which are fused with global features to precisely extract clothing-invariant representations. During inference，integrating head feature matching with identity feature matching further enhances the model discriminative capability. Experimental results show that the PMGM model achieves 5.7% improvement in mAP and 6.1% improvement in Rank-1 on the PRCC dataset compared to baseline models.

Keywords： person re-identification； computer vision; deep learning；attention mechanism

随着视频监控应用需求的增长和深度学习的兴起，换装行人重识别任务逐步成为学界研究的焦点。（剩余7739字）