Abstract
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.
Abstract (translated)
为了满足道路交通流量监测和公共安全管理的需要，城市道路中广泛分布着视频监控摄像头。然而，每个摄像头捕捉到的信息都是孤立的，这使得有效使用变得困难。车辆识别是指在另一个摄像头中出现的车辆，这可以关联多个摄像头捕捉到的信息。尽管车牌识别在某些应用中扮演着重要角色，但在某些场景中，基于车辆外观的识别方法可能更合适。主要挑战是，车辆外观信息的特征是高跨类相似度和大类内差异。因此，仅依靠车辆外观信息很难准确地区分不同车辆。此时，通常需要引入一些额外的信息，例如空间-时间信息。然而，在桥场景中，车辆相对位置的变化很少。在本文中，我们提出了一个基于羽量相似度的车辆识别方法，该方法利用了目标车辆周围的车辆信息来提高车辆识别的准确性。当车辆相对位置保持不变，羽量适当时，我们在实验中获得了VeRi数据集中平均相对改善率为204%的结果。接着，我们讨论了车辆在通过两个摄像头时相对位置变化的影响。我们提出了两个可以量化差异并建立联系的指标。虽然这个假设基于桥场景，但在其他场景中也是真实的，因为驾驶安全性和摄像头位置。
URL
https://arxiv.org/abs/2403.07752
