PAFM: pose-drive attention fusion mechanism for occluded person re-identification

Pedestrians are often occluded by various obstacles in public places, which is a big challenge for person re-identification. To alleviate the occlusion problem, we propose a Pose-drive Attention Fusion Mechanism (PAFM) that jointly fuses the discriminative features with pose-driven attention and spatial attention in an end-to-end framework. To simultaneously use global and local features, a multi-task network is constructed to realize multi-granularity feature representation. After anchoring the region of interest to the un-occluded spatial semantic information in the image through the spatial attention mechanism, some key points of the pedestrian's body are extracted using pose estimation and then fused with the spatial attention map to eliminate the harm of occlusion to the re-identification. Besides, the identification granularity is increased by matching the local features. We test and verify the effectiveness of the PAFM on Occluded-DukeMTMC, Occluded-REID and Partial-REID. The experimental results show that the proposed method has achieved competitive performance to the state-of-the-art methods.