A Cross-Period Network for Clothing Change Person Re-Identification

Pedestrian re-identification aims to identify the same target pedestrian among multiple non-overlapping cameras. However, in real scenarios, pedestrians often change their clothing features due to external factors such as weather and seasons, rendering traditional methods reliant on consistent clothing features ineffective. In this paper, we propose a Knowledge-Driven Cross-Period Network for Clothing Change Person Re-Identification, comprising three key components: 1) Knowledge-Driven Topology Inference Network: Leveraging knowledge graphs and graph convolution networks, this network captures spatio-temporal information between camera nodes. Knowledge embedding is introduced into the graph convolution network for effective topology inference; 2) Cross-Period Clothing Change Network: This network aggregates spatio-temporal information for clothing generation. By utilizing overall pedestrian clothing characteristics whthin logical topology cameras, it mitigates matching errors caused by external factors; and 3) Joint Optimization Mechanism: A collaborative approach involving both the topology inference network and cross-period clothing change network. Multi-camera logical topology offers auxiliary information and retrieval order for the clothing change network, while pedestrian re-identification results provide feedback to adjust the logical topology. Experimental analysis on datasets Celeb-ReID, PRCC, UJS-ReID, SLP, and DukeMTMC-ReID, demonstrates the effectiveness and robustness of our proposed model in addressing the challenges of pedestrian re-identification in scenarios involving changing clothing features.