基于视觉的桥梁场景下利用车队相似性的车辆重识别
Vision-based Vehicle Re-identification in Bridge Scenario using Flock Similarity
AI总结：
针对车辆重识别外观区分度低的问题，提出基于车队相似性的方法，利用相邻车辆信息提升准确率，在桥梁场景等相对位置稳定的场景下效果显著。
摘要：
受道路交通流量监测与公共安全管理需求驱动，城市道路中广泛部署了视频监控摄像头。但各摄像头直接采集的信息处于孤岛状态，难以得到有效利用。车辆重识别是指在另一摄像头下找到曾出现在某一摄像头中的车辆，可关联多摄像头采集的信息。虽然车牌识别在部分应用中发挥着重要作用，但在一些场景下，基于车辆外观的重识别方法更为适用。该任务的主要挑战在于车辆外观数据存在类间相似度高、类内差异大的特点，因此仅依靠车辆外观信息难以准确区分不同车辆，此时往往需要引入时空信息等额外信息。而在桥梁场景中，车辆经过相邻两个摄像头时相对位置极少发生变化。本文提出一种基于车队相似性的车辆重识别方法，通过利用目标车辆的相邻车辆信息提升车辆重识别准确率。在车辆相对位置保持不变且车队规模合适的条件下，实验中我们在VeRi数据集上取得了204%的平均相对提升。随后，本文探讨了车辆经过两个摄像头时相对位置变化幅度的影响，提出了两个可用于量化该差异的指标，并建立了二者之间的关联。尽管这一假设基于桥梁场景，但受驾驶安全与摄像头布设位置影响，该假设在其他场景中也通常成立。
英文摘要：
Due to the needs of road traffic flow monitoring and public safety management, video surveillance cameras are widely distributed in urban roads. However, the information captured directly by each camera is siloed, making it difficult to use it effectively. Vehicle re-identification refers to finding a vehicle that appears under one camera in another camera, which can correlate the information captured by multiple cameras. While license plate recognition plays an important role in some applications, there are some scenarios where re-identification method based on vehicle appearance are more suitable. The main challenge is that the data of vehicle appearance has the characteristics of high inter-class similarity and large intra-class differences. Therefore, it is difficult to accurately distinguish between different vehicles by relying only on vehicle appearance information. At this time, it is often necessary to introduce some extra information, such as spatio-temporal information. Nevertheless, the relative position of the vehicles rarely changes when passing through two adjacent cameras in the bridge scenario. In this paper, we present a vehicle re-identification method based on flock similarity, which improves the accuracy of vehicle re-identification by utilizing vehicle information adjacent to the target vehicle. When the relative position of the vehicles remains unchanged and flock size is appropriate, we obtain an average relative improvement of 204% on VeRi dataset in our experiments. Then, the effect of the magnitude of the relative position change of the vehicles as they pass through two cameras is discussed. We present two metrics that can be used to quantify the difference and establish a connection between them. Although this assumption is based on the bridge scenario, it is often true in other scenarios due to driving safety and camera location.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
