Part-Attention Based Model Make Occluded Person Re-Identification Stronger

The goal of occluded person re-identification (ReID) is to retrieve specific pedestrians in occluded situations. However, occluded person ReID still suffers from background clutter and low-quality local feature representations, which limits model performance. In our research, we introduce a new framework called PAB-ReID, which is a novel ReID model incorporating part-attention mechanisms to tackle the aforementioned issues effectively. Firstly, we introduce the human parsing label to guide the generation of more accurate human part attention maps. In addition, we propose a fine-grained feature focuser for generating fine-grained human local feature representations while suppressing background interference. Moreover, We also design a part triplet loss to supervise the learning of human local features, which optimizes intra/inter-class distance. We conducted extensive experiments on specialized occlusion and regular ReID datasets, showcasing that our approach outperforms the existing state-of-the-art methods.

Occluded Cloth-Changing Person Re-Identification

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.