Abstract: Vehicle re-identification refers to re-identifying the same vehicle from different cameras. The result of vehicle re-identification is easily affected by other factors such as vehicle angle and illumination, which is a very challenging task. Many vehicle re-identification methods pay too much attention to the global features of the vehicle, but ignore the local resolution features of the vehicle image, which result in the problem of low accuracy of vehicle re-recognition. To solve this problem, this paper proposes a vehicle re-identification method integrating non-local attention and multi-scale features. The attention mechanism is used to obtain vehicle salient features and integrate multi-scale features, so as to improve the retrieval accuracy of vehicle re-identification. Firstly, the backbone feature extraction network and attention module are used to obtain the significant fine-grained features of vehicles. Then, the feature is divided into multiple branches for metric learning. The local and global features of vehicles are learned respectively, and the global features and fine-grained local features are fused to construct the features of vehicle re-identification. Finally, this method is used to extract the characteristics of different vehicles and calculate the similarity of different vehicles and judge whether they have the same identity. The experimental results show that the vehicle re-identification algorithm using attention mechanism and local features has higher accuracy.

Key words: non-local attention, local features, vehicle re-identification, neural network