# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
###### Abstract
Specifically, the author combines the adversarial training mechanism and the vehicle parsing classifier to propose a viewpoint-aware attention model for obtaining the attention map from the input image, and considering that the attention map is a single-view feature, the author reported a multi-view generative network based on this attention map that is designed to infer global features for different viewpoints. [PERSON] et al. [10] proposed a quadruple direction pooling method. Specifically, in the first stage of the framework proposed by the author, the basic deep learning architecture is used to extract features, and then in the second stage, the feature map is pooled in four directions (horizontal, vertical, diagonal, anti-corner). The feature maps of the corresponding directions are obtained, and these direction feature maps are spatially normalized and connected together as the quadruple direction learning features for vehicle re-identification. [PERSON] et al. [11] proposed a multi-branch network for processing vehicle images from different viewpoints. With the help of a classifier, the input image is divided into three categories: front, side, and rear views, and the weighted weight on the corresponding branch is increased. When the image passes through the three branches in parallel, the features are weighted and fused. Recognizable features include car window textures, placement of items in the car, etc., but these feature areas are small. Accurate mining of these features is a way to improve vehicle recognition. [PERSON] et al. [12] proposed to use detection network to preprocess the regions where discriminative features may exist in the vehicle. In this paper, the author divides 21 detection areas and proposes a vehicle re-identification data set with 21 area labels for training the detection network. This is the first dataset labeled with so many fine-grained attributes through which the network is able to learn vehicle discriminative features useful for vehicle identification. In addition, the author used the single-stagedetection method (SSD) as the baseline model for detection on the constructed data set and achieved good results after making modifications based on this. The above methods are represented by vehicle re-identification, indicating that the features extracted by deep learning can adequately process the vehicle appearance information and angle information for the re-identification work to obtain good results. Even so, the above methods still have many problems. For example, under different conditions, the resolution of the vehicle pictures captured by the acquisition device is not enough when the weather is affected and the lighting conditions change. Obstacles are severely occluded, which seriously affects detection and recognition accuracy. Different vehicle angles captured by different acquisition devices cause the appearance of the same vehicle to be deformed. Different brands of the same types of vehicles having similar colors and different vehicles with similar models make it easy to match the vehicles incorrectly. A new vehicle re-identification algorithm to solve the above problems is put forward in this paper. The key point of this method is that it integrates the attention mechanism. The salient features of the vehicle are extracted by using shallow layer and deep layer joint attention unit, named SDLAU, which is a joint attention unit composed of a global spatial attention module and a channel attention module. It can be applied to different convolutional layers without changing the original structure of standard CNN. At the same time, a joint local module is incorporated into the channel attention mechanism to obtain local vehicle features. This design allows the transfer learning method to be immediately applied to the proposed model. SDLAU seamlessly improves the performance of standard CNN architectures such as ResNet when solving the vehicle re-identification task.