Multi-scale joint learning for person re-identification

The existing person re-identification approaches mainly focus on learning person's local features to match a specific pedestrian across different cameras. However, in the presence of incomplete conditions of pedestrian data such as motion or occlusion of human body parts, background interference, etc., it leads to an increase in the probability of partial loss of pedestrian recognition information. This paper presents a multi-scale joint learning method to extract the fine-grained person feature. This method consists of three subnets, i. e. coarse-grained global feature extraction subnet, fine-grained global feature extraction subnet, and fine-grained local feature extraction subnet. The coarse-grained global feature extraction subnet enhances the diversity of the global feature by fusing semantic information at different levels. The fine-grained global branching unites all local features to learn the correlation among local components of a pedestrian while describing the global features at a fine-grained level. The fine-grained local feature extraction subnet enhances robustness by traversing local features and finding out pedestrian non-significant information. Comparative experiments have been conducted to evaluate the performance of the proposed method against state-of-the-art methods on Market1501, DukeMTMC-ReID, and CUHK03 person re-identification datasets. The experimental results show that the proposed method has the best performance. © 2021, Editorial Board of JBUAA. All right reserved.