Pedestrian Re-Identification Based on CNN and TransFormer Multi-scale Learning

Person Re-IDentification (ReID) aims to retrieve specific pedestrian targets across surveillance cameras. For the purpose of aggregating the multi-granularity features of pedestrian images and further solving the problem of deep feature mapping correlation, Person Re-Identification based on CNN and TransFormer Multi-scale learning (CTM) is proposed. The CTM network is composed of a global branch, a deep aggregation branch and a feature pyramid branch. Global branch extracts global features of pedestrian images, and extracts hierarchical features with different scales. The deep aggregation branch aggregates recursively the hierarchical features of CNN and extracts multi-scale features. The feature pyramid branch is a two-way pyramid structure, under the attention module and orthogonal regularization operation, it can significantly improve the performance of the network. Experiments on three large scale datasets show the effectiveness of CTM. On the Market1501, DukeMTMC-reID and MSMT17 datasets, mAP/Rank-1 reached 90.2%/96.0%, 82.3%/91.6% and