Cross-modality person re-identification based on convolutional attention mechanism and multi-loss combination

Cross-modal person re-identification (Re-id) under infrared and visible light (RGB-IR) is of great significance for modern video surveillance, especially nighttime surveillance. The existing research results in the field of single-modal person re-identification have reached a high level. However, in addition to common problems such as lighting conditions, human poses, camera angles, etc., the difficulty of cross-modal person re-identification mainly lies in the simultaneous existence of huge differences between modalities and intra-modal variation within modalities. A cross-modal person re-identification method based on cumulative attention mechanism and joint multi-loss. This method is based on the dual-stream network structure. First, the first three convolutional layers of the Resnet50 network are used in the two branches of the dual-stream network to extract the shallow features of pedestrian images, and then the convolutional attention mechanism module is embedded to suppress the extraction of irrelevant information such as color., and fuse the middle-level features and the final features acquired by the branch backbone network to improve the discrimination of the acquired features. Finally, the bidirectional cross-modal triplet loss and the identity loss are used to jointly constrain the dual-stream network to speed up the convergence of the network model and effectively deal with the inter-modal differences. Differences as well as intra-class variation. The experimental results show that the method proposed in this paper can effectively improve the accuracy of cross-modal person re-identification problem.