Vehicle re-identification (ReID) endeavors to associate vehicle images collected from a distributed network of cameras spanning diverse traffic environments. This task assumes paramount importance within the spectrum of vehicle-centric technologies, playing a pivotal role in deploying Intelligent Transportation Systems (ITS) and advancing smart city initiatives. Rapid advancements in deep learning have significantly propelled the evolution of vehicle ReID technologies in recent years. Consequently, undertaking a comprehensive survey of methodologies centered on deep learning for vehicle re-identification has become imperative and inescapable. This paper extensively explores deep learning techniques applied to vehicle ReID. It outlines the categorization of these methods, encompassing supervised and unsupervised approaches, delves into existing research within these categories, introduces datasets and evaluation criteria, and delineates forthcoming challenges and potential research directions. This comprehensive assessment examines the landscape of deep learning in vehicle ReID and establishes a foundation and starting point for future works. It aims to serve as a complete reference by highlighting challenges and emerging trends, fostering advancements and applications in vehicle ReID utilizing deep learning models.

翻译：车辆重识别（ReID）旨在关联来自分布在多样交通环境中的摄像头网络的车辆图像。该任务在车辆相关技术体系中至关重要，在部署智能交通系统（ITS）和推进智慧城市倡议中发挥着关键作用。近年来，深度学习的快速发展显著推动了车辆ReID技术的演进。因此，对基于深度学习的车辆重识别方法进行全面的综述已变得必要且不可避免。本文深入探讨了应用于车辆ReID的深度学习技术。它概述了这些方法的分类，包括监督方法和无监督方法，深入分析了这些类别中的现有研究，介绍了数据集和评估标准，并阐述了未来的挑战与潜在研究方向。这一全面评估审视了深度学习在车辆ReID领域的格局，并为未来工作奠定了基础与起点。旨在通过突出挑战与新兴趋势，促进利用深度学习模型的车辆ReID的进步与应用，从而作为一份完整的参考文献。