Semantic guidance attention network for occluded person re-identification - Xuena REN; Dongming ZHANG; Xiuguo BAO; Bing LI (Tongxin xuebao)
To solve the problem of misalignment and mismatch in occluded person Re-ID, SGAN (semantic guided attention network) was proposed.In SGAN, the semantic masks of pedestrians were used as supervision to learn the global and local features through the attention modules, and the training process was dynamically adjusted according to the visibility of local regions.In the inference stage, the part-to-part matching strategy was adopted to adaptively measure visible features based on the feature visibility, which was obtained based on the learned masks from the attention modules.Experimental results show that the average accuracy of SGAN on the holistic datasets is better than most advanced models.Additionally, it is tolerant of occlusions and largely outperforms existing person Re-ID methods on two larger-scale complex occlusion datasets (Occluded-DukeMTMC and P-DukeMTMC-reID).