Cross-erasure enhanced network for occluded person re-identification

Occluded person re-identification is one of the most challenging tasks in safety monitoring. Most existing methods for occluded person re-identification rely on external auxiliary models, which cannot handle non-target pedestrian occlusions and ignore the contextual information of pedestrian images. To address the above issues, we propose a cross-erasure enhanced network (CENet) for occluded person re-identification. To be specific, we propose a feature map cross-erasure module (FMCM) that can simulate obstacle occlusion and non-target pedestrian occlusion in real scenes by erasing feature maps. Meanwhile, we design an occluded-aware mixed attention module (OMAM), which empowers the network to efficiently capture features from non-occluded areas. Finally, we propose a full-view enhancement module (FEM) to extract discriminative features of pedestrian images by parsing the contextual information of the images. Comprehensive experimental outcomes on both occluded and holistic datasets affirm the effectiveness of our method.