MBA-Net: multi-branch attention network for occluded person re-identification

Occluded person re-identification (ReID) aims to retrieve the same pedestrian from partially occluded pedestrian images across non-overlapping cameras. Current state-of-the-art methods generally use auxiliary models to obtain non-occluded regions, which not only result in more complex models, but also cannot effectively handle the more generalized ReID task. To this end, a Multi-Branch Attention Network (MBA-Net) is proposed to achieve multi-level refinement of features through an end-to-end multi-branch framework with attention mechanisms. Specifically, we first achieve preliminary feature refinement through a backbone network with a non-local attention mechanism. Then, a two-level multi-branch architecture in MBA-Net is proposed with two-level features refinement to obtain aware local discriminative features from the self-attention branch, non-occluded local complementary features from the cross-attention branch, and global features from the global branch. Finally, we can obtain retrieval features that are robust to occlusion by concatenating all the above features. Experimental results show that our MBA-Net achieves state-of-the-art performance on an occluded person ReID dataset Occluded-Duke and simultaneously achieves competitive performance on two general person ReID datasets Market-1501 and DukeMTMC-ReID.