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Person re-identification (ReID) is the use of computer vision and machine learning techniques to determine whether the pedestrians in the two images under different cameras are the same person. It can also be regarded as a matching retrieval task for person targets in different scenes. The research focuses on how to obtain effective person features from images with occlusion, angle change, and target attitude change. Based on the present difficulties and challenges in ReID, the paper proposes a ReID method based on joint loss and multi-attention network. It improves the person re-identification algorithm based on global characteristics, introduces spatial attention and channel attention and constructs joint loss function, improving the characteristic extraction ability of the network and improving the model performance of person re-identification. It analyzes the validity and necessity of each module of the algorithm through the ablation experiment. In addition, it carries out training and evaluation on the two person re-recognition data sets Market1501 and MSMT17, and verifies the advantages of the proposed algorithm in contrast to other advanced algorithms.

Person Re-Identification Based on Joint Loss and Multiple Attention Mechanism