# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
###### Abstract  
The specific task of vehicle re-identification is how to quickly and correctly match the same vehicle in different scenarios. In order to solve the problem of inter-class similarity and environmental interference in vehicle images in complex scenes, one fusion attention method is put forward based on the idea of obtaining the distinguishing features of details--the mechanism for the vehicle re-identification method. First, the vehicle image is preprocessed to restore the image's attributes better. Then, the processed image is sent to ResNet50 to extract the features of the second and third layers, respectively. Then, the feature fusion is carried out through the two-layer attention mechanism for a network model. This model can better focus on local detail features, and global features are constructed and named SDLAU-Reid. In the training process, a data augmentation strategy of random erasure is adopted to improve the robustness. The experimental results show that the mAP and rank-k indicators of the model on VeRi-776 and the VehicleID are better than the results of the existing vehicle re-identification algorithms, which verifies the algorithm's effectiveness. vehicle re-identification; attention mechanism; key-point; local feature; feature fusion Article Research  
the critical sensitive area. However, the features extracted by hand can only be used in a single hypothetical scene, which can no longer satisfy today's technology development. In today's vehicle re-identification models in complex scenarios, the current vehicle feature extraction methods mainly rely on deep learning methods by designing a specific network structure and using corresponding training tasks to guide the network to learn features for recognition. Beneficial for the construction of large-scale vehicle datasets, such as the VeRi-776 [1] dataset, VehicleID [2] dataset, Toy Car Re-ID [3] dataset, and VRIC [4] dataset, research on the re-identification of the vehicle recognition model has also been proposed. [PERSON] et al. [5] modeled the vehicle image information to form a multi-granularity relationship and thus proposed a model based on a multi-task framework, a multi-granularity-based list sorting, and pairwise sorting method. The constraints of granularity sorting solve the problem of a precise vehicle search. [PERSON] et al. [6] used the Siamese network to calculate the similarity of license plates based on the dual constraints of spatiotemporal features and appearance features, which improved the accuracy of vehicle re-identification. [PERSON] et al. [7] used CNN (convolutional neural networks, CNN) to learn vehicle identity, color and type information. They obtained the vehicle's perspective based on the method of vehicle pose estimation to obtain more robust vehicle feature information, thereby improving the algorithm's performance. [PERSON] et al. [8] proposed the idea of dismantling the car body and matching it in blocks. With the help of a body analysis network, the author decomposes the body features into four regions: the front, the side, the rear, and the roof and obtains the feature vectors of the four respective regions. When matching, the same area of the two vehicles is matched and the common visual score of the area is obtained, and then the common visual score of the four areas is fused as the local feature distance of the two vehicles. Combining the local feature distance with the global feature distance with a certain weight is the feature distance of the two vehicles. This method has achieved good results, but it depends on the performance of the vehicle analysis network. Since the re-identification task is to retrieve images across cameras, it is necessary to take into account the impact of viewpoint conversion. [PERSON] et al. [9] proposed a vami model which can use the single-view feature of the query image to generate multi-view features of the same identity vehicle.