In the current research on cross-modal person re-identification technology, most existing methods reduce cross-modal differences by using single modal original visible light images or locally shared features of adversarially generated images, resulting in a lack of stable recognition accuracy in infrared image discrimination due to the loss of feature information. In order to solve this problem, A cross-modal person re-identification method based on swappable hybrid random channel augmentation with structured dual attention is proposed.The visual image after channel enhancement is used as the third mode, and the single channel and three channels random hybrid enhancement extraction of visible image is performed through the Image Channel Swappable random mix Augmentation (I-CSA) module, so as to highlight the structural details of pedestrian posture, Reduce modal differences in learning. The Structured joint Attention Feature Fusion (SAFF) module provides richer supervision for cross-modal Feature learning, and enhances the robustness of shared features in modal changes, under the premise of focusing on the structural relationship of pedestrian attitudes between modes. Under the single shot setting of full search mode in the SYSU-MM01 dataset, Rank-1 and mAP reached 71.2％and 68.1％, respectively, surpassing similar cutting-edge methods.
关键词
行人重识别/跨模态/混合通道增强/联合注意力/特征融合
Key words
Person Re-identification/Cross-modal/Hybrid channel enhancement/Joint attention/Feature fusion
维普
万方数据