  Cloth-changing person re-identification aims to retrieve and identify
spe-cific pedestrians by using cloth-unrelated 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 person re-identification methods is sig-nificantly
degraded due to the reduction of discriminative cloth-unrelated 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 propose occluded
cloth-changing person re-identification as a new task. We constructed two
occluded cloth-changing person re-identification datasets: Occluded-PRCC and
Occluded-LTCC. The da-tasets can be obtained from the following link:
https://github.com/1024AILab/Occluded-Cloth-Changing-Person-Re-Identification.
