Pedestrian Re-identification Based on Spatial Transformation and Multi-methods Feature Fusion

Aiming at the problem that the spatial features of pedestrian images are not aligned in current pedestrian re-identification and the network model cannot fully express the pedestrian information due to pose changes and occlusions, a method based on spatial transformation and multi-methods feature fusion is proposed. Firstly, for the pedestrian re-identification system, a processing method for enhancing the retrieval of pedestrians is provided, and the pedestrian images with more noise to be retrieved are denoised by means of side-window filtering; secondly, the spatial transformation network is improved. Channel attention and self-constrained branches are introduced to automatically align pedestrian spatial features to solve the problem of inconsistency in spatial semantic information caused by unaligned pedestrian image regions; then, multi-scale features are extracted from different deep layers of the backbone network, and coordinates attention and batch normalization of instances are integrated into different deep branches. Finally, the features of each branch are fused to obtain feature information with high representation ability. During the network training process, the dual loss function strategy is used to jointly optimize the model. Multiple experiments show that the proposed method has a higher recognition rate than other existing methods.