Person re-identification driven by global attention mechanism and relation network.

To address the issue of the insufficient ability of existing models in the extraction of crucial features in person re-identification (ReID) caused by occlusions and pose variations, a person re-identification algorithm driven by a global attention mechanism and relation network is proposed. Firstly, in the backbone network, a global attention module is embedded into the ResNei-50 network to capture weight information in both spatial and channel dimensions. Secondly, in the relation network, multiple local features of different scales are obtained through horizontal partitioning. Two modules are designed to extract the global contrast features and relation features among local features. The local relation features are fused with global contrast information and fed into the classification network. Finally, in the loss optimization stage, a joint loss consisting of Circle Loss, triplet loss, and cross-entropy loss is employed for network training. Experiments are conducted on five commonly used datasets, and the results are analyzed. The proposed algorithm achieves a rank-1 accuracy of 63.2% and 95.4%, as well as mAP scores of 53.8% and 88.2% on the Occluded-DukeMTMC and Market 1501 datasets, respectively, demonstrating its advancement. [ABSTRACT FROM AUTHOR]