Dual-Branch Person Re-Identification Algorithm Based on Multi-Feature Representation

Abstract

Person re-identification can identify specific pedestrians across cameras and solve the visual limitations of a single fixed camera scene. It achieves trajectory analysis of target pedestrians, facilitating case analysis by public security personnel. Person re-identification has become a challenging problem due to occlusion, blur, posture change, etc. The key to affecting the accuracy is whether sufficient information of pedestrians can be obtained. Most existing deep learning methods only consider a single category of characteristics, resulting in problems of information loss and single feature expression. This paper proposed a dual-branch person re-identification algorithm based on multi-scale and multi-granularity branches to obtain complete pedestrian information, called MSMG-Net. The multi-scale branch extracted features from different layers and fused them through the bidirectional cross-pyramid structure. It made up for the loss of information caused by using only high-level semantic features. The multi-granularity branch combined the convolutional neural network (CNN) and Transformer module based on improved self-attention mechanisms to obtain global information, and it performed horizontal segmentation of feature maps to obtain local information. It combined global and local features and solved the problem of single feature expression. The experimental results show that mAP/Rank-1 of MSMG-Net reached 88.6%/96.3% on the Market-1501 dataset and 80.1%/89.9% on the DukeMTMC-ReID dataset. Compared with many state-of-the-art methods, the performance of the proposed algorithm was improved significantly.