Vehicle re-identification (Re-ID) technology plays an important role in the intelligent transportation system for smart city. Due to various uncertain factors in the real-world scenarios, (e.g., resolution variation, viewpoint variation, illumination changes, occlusion, etc., vehicle Re-ID is a very challenging task. To resist the adverse effect of resolution variation, a joint pyramid feature representation network (JPFRN) for vehicle Re-ID is proposed in this paper. Based on the consideration that various convolution blocks with different depths hold different resolutions and semantic information of the vehicle image, the proposed JPFRN method employs a base network to obtain multi-resolution vehicle features in the first stage. Then, a pyramid feature representation scheme is developed to reconstruct and integrate the obtained multi-resolution vehicle features together. Finally, these pyramid features are jointly represented for learning a more discriminative feature under the supervision of joint Triplet loss and softmax loss. Extensive experimental results on two commonly-used vehicle databases (i.e., VehicleID and VeRi) show that the proposed JPFRN is superior to multiple recently-developed vehicle Re-ID methods.

车辆重新识别（Re-ID）技术在智慧城市的智能交通系统中起着重要作用。由于现实场景中的各种不确定因素（例如，分辨率变化，视点变化，照明变化，遮挡等），车辆Re-ID是一项非常具有挑战性的任务。为了抵抗分辨率变化的不利影响，提出了一种用于车辆Re-ID的金字塔特征表示网络（JPFRN），在考虑到不同深度的各种卷积块具有不同的分辨率和车辆图像语义信息的情况下，提出的JPFRN方法采用了一种基础网络来获得。第一阶段的多分辨率车辆功能。开发了金字塔特征表示方案，以将获得的多分辨率车辆特征重建和集成在一起。最后，这些金字塔特征被联合表示，以便在三重态损失和softmax损失的联合监督下学习更具区分性的特征。在两个常用的车辆数据库（即VehicleID和VeRi）上的大量实验结果表明，所提出的JPFRN优于最近开发的多种车辆Re-ID方法。