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Person re-identification (re-ID) is a challenging task in real-world. Besidesthe typical application in surveillance system, re-ID also has significantvalues to improve the recall rate of people identification in content video (TVor Movies). However, the occlusion, shot angle variations and complicatedbackground make it far away from application, especially in content video. Inthis paper we propose an entropy based semantic feature alignment model, whichtakes advantages of the detailed information of the human semantic feature.Considering the uncertainty of semantic segmentation, we introduce a semanticalignment with an entropy-based mask which can reduce the negative effects ofmask segmentation errors. We construct a new re-ID dataset based on contentvideos with many cases of occlusion and body part missing, which will bereleased in future. Extensive studies on both existing datasets and the newdataset demonstrate the superior performance of the proposed model.

ESA-ReID: Entropy-Based Semantic Feature Alignment for Person re-ID