Cloth-changing person re-identification aims to retrieve and identify spe-cific pedestrians by using cloth-irrelevant features in person cloth-changing scenarios. However, pedestrian images captured by surveillance probes usually contain occlusions in real-world scenarios. The perfor-mance of existing cloth-changing re-identification methods is significantly degraded due to the reduction of discriminative cloth-irrelevant features caused by occlusion. We define cloth-changing person re-identification in occlusion scenarios as occluded cloth-changing person re-identification (Occ-CC-ReID), and to the best of our knowledge, we are the first to pro-pose occluded cloth-changing person re-identification as a new task. We constructed two occluded cloth-changing person re-identification datasets for different occlusion scenarios: Occluded-PRCC and Occluded-LTCC. The datasets can be obtained from the following link: https://github.com/1024AILab/Occluded-Cloth-Changing-Person- Re-Identification.

翻译：换装行人重识别旨在通过利用与衣物无关的特征，在行人换装场景中检索并识别特定行人。然而，实际场景中监控探头拍摄的行人图像通常存在遮挡。由于遮挡导致具有判别性的衣物无关特征减少，现有换装重识别方法的性能显著下降。我们将遮挡场景下的换装行人重识别定义为遮挡换装行人重识别，据我们所知，这是首次提出遮挡换装行人重识别这一新任务。针对不同遮挡场景，我们构建了两个遮挡换装行人重识别数据集：Occluded-PRCC和Occluded-LTCC。数据集可通过以下链接获取：https://github.com/1024AILab/Occluded-Cloth-Changing-Person-Re-Identification。