Abstract: As one of the key technologies of intelligent transportation systems, vehicle re-identification (Re-ID) aims to retrieve the same vehicle from different monitoring scenes and plays an important role in building a safe and smart city. With the continuous development of computer vision, the Re-ID method of using supervised learning suffers from the problems of strong reliance on manual annotation in the training process and weak scene generalization ability, so unsupervised learning of vehicle Re-ID gradually becomes the focus of research in recent years. Firstly, the present mainstream vehicle Re-ID datasets and the commonly used model evaluation metrics are introduced. Then, latest unsupervised learning-based vehicle Re-ID methods are grouped into two categories: gene-rative adversarial networks and clustering algorithms according to the current research ideas. Starting from the problems of domain deviation, cross-view deviation and insufficient information of data samples, the former is further divided into three categories of style transfer, multi-view generation, and data augmentation. For the labeling pro-blem, the latter can be divided into two categories of pseudo-labeled unsupervised domain adaptation and no label infor-mation required. With problem solving as the starting point, the fundamentals, advantages and disadvantages, and performance results of each type of method on mainstream datasets are summarized. Finally, the challenges faced by the current unsupervised learning for vehicle Re-ID are analyzed, and the future work in this research direction is prospected.

Key words: intelligent transportation, vehicle re-identification, unsupervised learning, generative adversarial networks, clustering

摘要： 车辆重识别作为智能交通系统的关键技术之一，旨在从不同监控场景下识别同一车辆，对构建平安智慧城市起着重要作用。随着计算机视觉技术的不断发展，使用监督学习的重识别方法存在训练过程对人工标注依赖强、场景泛化能力弱的问题，因此无监督学习的车辆重识别逐渐成为近年来研究的重点。首先，介绍了当前主流的车辆重识别数据集以及常用的模型评价指标。然后，系统梳理了近几年基于无监督学习的车辆重识别方法，根据目前的研究思路将这些方法归纳为生成对抗网络和聚类算法两大类；从域偏差、跨视域偏差以及数据样本信息不足的问题出发，将前者进一步分为风格转换、多视角生成和数据增强三类；又针对标签的问题，将后者分为伪标签的无监督域适应和无需标签信息两类；以解决问题为着手点，总结出每类方法的基本原理、优缺点以及在主流数据集上的性能结果。最后，讨论分析了目前无监督学习的车辆重识别所面临的挑战，并对该研究方向的未来工作进行展望。

关键词: 智能交通, 车辆重识别, 无监督学习, 生成对抗网络, 聚类