OC4-ReID: Occluded Cloth-Changing Person Re-Identification
<|reference_start|>OC4-ReID: Occluded Cloth-Changing Person Re-Identification: The study of Cloth-Changing Person Re-identification (CC-ReID) focuses on retrieving specific pedestrians when their clothing has changed, typically under the assumption that the entire pedestrian images are visible. Pedestrian images in real-world scenarios, however, are often partially obscured by obstacles, presenting a significant challenge to existing CC-ReID systems. In this paper, we introduce a more challenging task termed Occluded Cloth-Changing Person Re-Identification (OC4-ReID), which simultaneously addresses two challenges of clothing changes and occlusion. Concretely, we construct two new datasets, Occ-LTCC and Occ-PRCC, based on original CC-ReID datasets to include random occlusions of key pedestrians components (e.g., head, torso). Moreover, a novel benchmark is proposed for OC4-ReID incorporating a Train-Test Micro Granularity Screening (T2MGS) module to mitigate the influence of occlusion and proposing a Part-Robust Triplet (PRT) loss for partial features learning. Comprehensive experiments on the proposed datasets, as well as on two CC-ReID benchmark datasets demonstrate the superior performance of proposed method against other state-of-the-art methods. The codes and datasets are available at: https://github.com/1024AILab/OC4-ReID.<|reference_end|>
@article{chen2024oc4-reid:,
    title={OC4-ReID: Occluded Cloth-Changing Person Re-Identification},
    author={Zhihao Chen, Yiyuan Ge, Ziyang Wang, Jiaju Kang, Mingya Zhang},
    journal={arXiv preprint arXiv:2403.08557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08557},
    primaryClass={cs.CV}
}