Occlusion Simulation and Token-Constrained Feature Coupling Network for Occluded Person Re-Identification

Occluded person re-identification (Re-ID) aims to learn the features of pedestrians with different identities under occlusion, and is a pivotal technology for intelligent security surveillance systems for the Internet of Things (IoT). In real scenarios, occlusion may occur at any spatial location and exhibits irregularity, occluded people re-identification (Re-ID) remains a challenging task. Most existing methods distinguish visible body parts by leveraging location-based segmentation or external cues, but they are often inefficient or increase network complexity. To address these problems, we propose an Occlusion Simulation and Token-Constrained Feature Coupling Network (OSTCNet). Specifically, the Occlusion Simulation Based on Block Mixing (OSBBM) strategy segments pedestrian images with different labels into blocks during the training phase and proportionally blends the image blocks based on coordinates, thereby generating occluded samples with diverse occlusion patterns. Additionally, we propose the Local-Global Feature Coupling (LGFC) module to perform multi-scale coupling of local features and global representations, enhancing classification accuracy and capturing more comprehensive feature information. Given the lack of explicit constraints in ViT to differentiate the similarity between class token and patch tokens, which can cause accumulation of similar information in the feature embedding space, we introduce the Token Orthogonal Embedding (TOE) module to enforce constraints on class token, ensuring representational differences from patch tokens. Experimental results on occluded, partial, and holistic ReID datasets validate the effectiveness of our method. Specifically, on the Occluded-DukeMTMC dataset, OSTCNet achieves Rank-1 accuracy of 75.2% and mAP score of 65.7%.