A Person Re-Identification Method with Multi-Scale and Multi-Feature Fusion

In order to solve the problem of insufficient expression of pedestrian features and ignoring the semantic relevance of adjacent areas in person re-identification, a multi-scale and multi-feature fusion person re-identification model is proposed. Firstly, feature maps containing different information are obtained by multi-branch structure. Secondly, the semantic correlation between local features is emphasized by combining adjacent local regions. Finally, combining the advantages of maximum pooling and average pooling,the model can learn more comprehensive feature information from different directions. Experiments are carried out on Market-1501, DukeMTMC-reID and MSMT17 respectively, it can be evaluated that the model can effectively extract pedestrian feature and has high recognition accuracy under different lighting and camera angles.