Existing methods in Person Re-Identification (ReID) often fail when simultaneously confronted with occlusions and clothing changes. In this paper, we introduce a challenging yet practical task called Occluded Cloth-Changing Re-ID (OCCRe-ID/). We propose Occlusion-aware Appearance and Shape Reasoning, the first framework OCCReID. We first propose an occlusion synthesis strategy to expose the model to real-world occlusion variations. We mitigate clothing changes by coupling silhouette-based body shape information with appearance. Unlike previous works that directly leverage unreliable features extracted from occluded images by off-the-shelf backbones, we propose an occlusion-awareness strategy to handle occlusions for ReID. An occlusion detection module is elaborately designed to generate occlusion-aware feature, which is then used to guide the framework to reason robust appearance and shape features. Extensive experiments demonstrate the superiority of our framework over both cloth-changing Re-ID and occluded Re-ID methods.

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.

Person Re-Identification (Re-ID) aims at matching a person captured by a non-overlapping camera system. Real-world Re-ID presents challenges like clothing changes and occlusions, which limits the applicability of traditional appearance-based methods. Cloth-Changing Re-ID (CCRe-ID) methods that rely on cloth-invariant modalities, such as shape, gait, etc., ignore occlusions and fail to mine the complementary relationship across modalities. Meanwhile, methods that explicitly focus on occlusion management struggle with cloth-changing scenarios. To address these, we propose ACML: Attention-based Cross-Modality Learning, the first framework to tackle both clothing changes and occlusion in Re-ID. Our lightweight framework comprises a unified network with cascaded Cross-Attention Blocks that extracts appearance and shape features collaboratively, enhancing robustness under clothing changes, viewpoint variations, and poor illumination conditions. Inputs to the network are produced by our novel occlusion synthesis module, which not only helps exposing the model to occlusions but also guides the model to adaptively attend to informative cues and reduce noise. Experiments demonstrate the effectiveness of ACML on both CCRe-ID and occluded Re-ID datasets.

Traditional approaches for Person Re-identification (ReID) rely heavily on modeling the appearance of persons. This measure is unreliable over longer durations due to the possibility for changes in clothing or biometric information. Furthermore, viewpoint changes significantly degrade the matching ability of these methods. In this paper, we propose "Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification" (CVSL) to address these challenges. Our method robustly extracts local and global texture-invariant human body shape cues from 2D pose using the Relational Shape Embedding branch, which consists of a pose estimator and a shape encoder built on a Graph Attention Network. To enhance the discriminability of the shape and appearance of identities under viewpoint variations, we propose Contrastive Viewpoint-aware Losses (CVL). CVL leverages contrastive learning to simultaneously minimize the intra-class gap under different viewpoints and maximize the inter-class gap under the same viewpoint. Extensive experiments demonstrate that our proposed framework outperforms state-of-the-art methods on long-term person Re-ID benchmarks.