Vehicle re-identification is an important part of intelligent transportation. Although much work has been done on this subject in recent years, vehicle re-identification is still a challenging task due to its obvious illumination change, high similarity between inter-class and great changes under different views. As discriminatory local areas and vehicle view information is the key to improving the above issues, it is desirable to create a model which considers both local details and cross-view situation. In this paper, we built a vehicle re-identification framework based on unsupervised local area detection and view discrimination. First, we combine the convolution features of multiple vehicle images by channel to generate joint channel features, and constructs a discriminating region detector unsupervised by clustering the channel covariance vectors generated between the joint channel features. In the next stage, the irregular shape detection results are converted to rectangular discriminative region by using a novel local region integration algorithm. These rectangular discriminative regions are fed into a multi-branch network to extract the local–global features which contains rich detail information. Furthermore, we generate the view features by regarding the detected discriminative areas as keypoints and construct the unsupervised view discriminator. By using the view information of the vehicle, we designed a view-discrimination based reranking algorithm, which effectively reduces the error rate of identification due to view variant. In order to prove the validity of our method, we have done extensive experiments on VehicleID and VERI-Wild dataset. Experimental results show that our method is superior to other existing vehicle re-identification algorithms.

车辆重新识别是智能交通的重要组成部分。尽管近年来在这个问题上已经做了很多工作，但是由于其明显的照明变化，车类之间的高度相似性以及不同观点下的巨大变化，车辆重新识别仍然是一项艰巨的任务。由于区分区域和车辆视图信息是改善上述问题的关键，因此希望创建一个同时考虑局部细节和交叉视图情况的模型。在本文中，我们建立了基于无监督局部检测和视图识别的车辆重新识别框架。首先，我们按通道组合多个车辆图像的卷积特征以生成联合通道特征，并通过对联合通道特征之间生成的通道协方差向量进行聚类，构造一个无监督的区分区域检测器。在下一阶段，通过使用新颖的局部区域积分算法将不规则形状检测结果转换为矩形判别区域。这些矩形判别区域被馈送到多分支网络中，以提取包含丰富详细信息的局部局部特征。此外，我们通过将检测到的判别区域视为关键点来生成视图特征，并构造无监督的视图判别器。通过使用车辆的视点信息，我们设计了一种基于视点区分的重新排序算法，该算法有效地降低了因视点变化而引起的识别错误率。为了证明我们方法的有效性，我们已经对VehicleID和VERI-Wild数据集进行了广泛的实验。实验结果表明，该方法优于其他现有的车辆重识别算法。