Person re-identification based on Multi-feature Fusion to Enhance Pedestrian Features

Abstract

Person re-identification (person re-ID) is one of the important contents of joint intelligent analysis based on surveillance video, which plays an important role in maintaining social public safety. The key challenge of person re-ID is to address the problem of large intra-class variations among the same person and small inter-class variations between different persons. To solve this problem, we propose a Person Re-identification Network Based on Multi-feature Fusion to Enhance Pedestrian Features (MFEFNet). This network, through global, attribute, and local branches, leverages the complementary information between different levels of pedestrian features, thereby enhancing the accuracy of person re-ID. Firstly, this network leverages the stability of attribute features to reduce intra-class variations and the sensitivity of local features to increase inter-class differences. Secondly, a self-attention fusion module is proposed to address the issue of small receptive fields caused by residual structures, thereby enhancing the ability to extract global features. Thirdly, an attribute area weight module is proposed to address the issue that different pedestrian attributes focus on different person regions. By localizing regions related to attributes, it reduces information redundancy. Finally, this method achieved 95.63% Rank-1 accuracy and 88.29% mAP on Market-1501 dataset, 90.13% Rank-1 accuracy and 79.85% mAP on DukeMTMC-reID dataset and 77.21% Rank-1 accuracy and 60.34% mAP on Occluded-Market dataset.