Occlusion-aware Cross-Attention Fusion for Video-based Occluded Cloth-Changing Person Re-Identification

Video-based Person Re-Identification (Re-ID) is an important task in video surveillance analysis. Real-world video-based Re-ID commonly suffers from clothing changes and occlusions, which severely degenerates performance of traditional Re-ID methods. In this paper, we introduce a challenging yet practical task called Video-based Occluded Cloth-Changing Re-ID (VOCCRe-ID). To tackle occlusions, we propose an occlusion synthesis strategy to expose the model to real-world occlusion variations. To mitigate unreliable appearance caused by clothing changes, we couple body shape information from the normalized silhouette sequence. Then, we propose a cross-attention fusion mechanism to capture the complementary relationships between appearance and shape under occlusions, thus enhancing Re-ID robustness. In addition, since there are no dataset for VOCCRe-ID, we build the large-scale Occluded-VCCR dataset which explicitly presents occlusions and contains the most clothing variations. Extensive experiments show that we achieve SOTA performance over previous methods.