Person Parsing-Driven and Text-Guided for Cloth-Changing Person Re-Identification

With the rapid development of the Visual Internet of Things (VIoT), person re-Identification (ReID) technology has made significant strides in research, particularly in the domains of urban security and intelligent surveillance. However, cloth-changing person re-identification (CC-ReID) poses significant challenges to traditional appearance-based methods due to frequent changes in clothing. To address this issue, this paper proposes a Person Parsing-Driven and Text-Guided for Cloth-Changing Person Re-identification (PT-ReID). This approach optimizes the image encoder of the CLIP model through a multi-branch design and leverages person parsing techniques to extract stable, clothing-invariant biological features. Additionally, the method incorporates Context Optimization (CoOp) technology, using learnable text descriptions as soft supervision to enhance cross-modal alignment. A saliency region loss mechanism is also introduced to minimize discrepancies between different feature representations, further improving the model’s ability to learn discriminative pedestrian features. Experimental results on three public datasets—PRCC, LTCC, and VC-Clothes—demonstrate that PT-ReID significantly outperforms state-of-the-art methods in Rank-1 accuracy and mean Average Precision (mAP) under cloth-changing scenarios, proving its effectiveness and robustness in handling complex clothing variations.