Pose-Guided Feature Restoration Transformer for Occluded Person Re-identification

The occluded person re-identification (ReID) remains a challenge due to feature loss in occluded regions, which not only introduces noise interference but also poses significant difficulties in feature alignment. To systematically address this issue, we propose an innovative Pose-guided Feature Restoration Transformer (PRT) framework, designed to adaptively restore features within occluded areas. Our approach commences with an occlusion sample augmentation strategy. By integrating real-world images with noisy images, we simulate a wide spectrum of complex occlusion scenarios, enhancing the model’s generalization ability across diverse occlusion conditions. Subsequently, based on visibility scores, we develop a method that fuses common and unique features to recover the characteristics of occluded keypoints. To further optimize feature transfer, an adaptive directional graph convolution method is introduced to construct higher-order feature representations, effectively alleviating feature degradation caused by occlusion. Finally, leveraging the attention mechanism of the Transformer, we integrate both global and local contextual information to enhance the overall feature representation comprehensively. Comprehensive experiments conducted on both occluded and holistic datasets demonstrate the superior effectiveness of our PRT framework.