Abstract
Vehicles, as one of the most common and significant objects in the real world, the researches on which using computer vision technologies have made remarkable progress, such as vehicle detection, vehicle re-identification, etc. To search an interested vehicle from the surveillance videos, existing methods first pre-detect and store all vehicle patches, and then apply vehicle re-identification models, which is resource-intensive and not very practical. In this work, we aim to achieve the joint detection and re-identification for vehicle search. However, the conflicting objectives between detection that focuses on shared vehicle commonness and re-identification that focuses on individual vehicle uniqueness make it challenging for a model to learn in an end-to-end system. For this problem, we propose a new unified framework, namely CLIPVehicle, which contains a dual-granularity semantic-region alignment module to leverage the VLMs (Vision-Language Models) for vehicle discrimination modeling, and a multi-level vehicle identification learning strategy to learn the identity representation from global, instance and feature levels. We also construct a new benchmark, including a real-world dataset CityFlowVS, and two synthetic datasets SynVS-Day and SynVS-All, for vehicle search. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods of both vehicle Re-ID and person search tasks.
Abstract (translated)
车辆作为现实世界中最常见和最重要的对象之一，利用计算机视觉技术对其进行的研究已取得了显著的进展，例如车辆检测、车辆重识别等。为了从监控视频中搜索感兴趣的车辆，现有的方法通常先进行预检测并将所有车辆补丁存储起来，然后再应用车辆重识别模型，这种方法资源消耗大且不够实用。在本研究中，我们的目标是实现联合检测和重识别以用于车辆搜索。然而，检测侧重于共享的车辆共性而重识别则侧重于个体车辆的独特性，这种矛盾的目标使得在一个端到端系统中学习成为一个挑战。 针对这个问题，我们提出了一种新的统一框架，名为CLIPVehicle，该框架包含一个双粒度语义区域对齐模块，利用视觉语言模型（VLMs）进行车辆区分建模，并且采用多级车辆识别学习策略来从全局、实例和特征级别上学习身份表示。此外，我们还构建了一个新的基准，包括一个现实世界的数据集CityFlowVS以及两个合成数据集SynVS-Day和SynVS-All，用于车辆搜索任务。 通过广泛的实验结果证明，我们的方法在车辆重识别和人员检索任务中均超过了当前最先进的方法。
URL
https://arxiv.org/abs/2508.04120
