Person Re-identification Method Based on Multi-view and Attention Mechanism

For person re-identification the appearance of photographs taken by different cameras at different viewpoints varies considerably. In this paper, a person re-identification method based on multi-view and attention mechanism is proposed. Firstly, IBN-Net is introduced as the backbone network based on ResNet50, and CA attention mechanism is embedded into the backbone network to obtain person information with more discriminative features. Then, global, spatial and channel features are combined in order to obtain multi-scale feature information and local features of people. It is also considered that the person is a non-rigid structure, and its pose and viewpoint changes can cause huge differences in person appearance features from one camera to another and can ultimately affect the accuracy of recognition. Therefore, a mesh-based view generator is introduced to generate multi-view person images, and multi-view image enhancement methods are utilized to reduce the sensitivity of the network to viewpoint. Finally, the model is optimized by introducing three loss functions. Experiments conducted on three publicly available benchmark datasets Market-1501, DukeMTMC-ReID and CUHK03, the experimental results show that our proposed model is at a high level in both single-domain and cross-domain tasks.