基于非局部注意力和局部特征的车辆重识别算法

车辆重识别是指从不同的摄像机来重新识别出同一辆车。车辆重识别非常容易受到车辆角度以及光照等其他因素的影响，是一项非常有挑战性的任务。许多车辆重识别方法都过分关注车辆全局特征，而忽略了车辆图像的局部有分辨力的特征，造成了车辆重识别精度不高的问题。针对这一问题，本文提出一种整合非局部注意力的和多尺度特征的车辆重识别方法，使用注意力机制获取车辆显著特征，并融合多尺度特征从而提高车辆重识别的检索精度。首先，使用骨干特征提取网络与注意力模块获取车辆的显著性细粒度特征。然后，将特征分为多个分支进行度量学习，分别学习车辆的局部与全局特征，将全局特征与细粒度的局部特征融合，构建车辆重识别的特征。最后，利用该方法提取不同车辆的特征，计算不同车辆的相似度，从而判断是否具有相同的身份。实验结果表明本文提出的车辆重识别算法具有更高的精度。

Vehicle Re-identification Method Based on Non-local Attention and Local Features

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.