Wang Wei, Xie Yujia, Tang Luliang
College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210023, China.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China.
Sensors (Basel). 2023 Aug 3;23(15):6909. doi: 10.3390/s23156909.
With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time.
随着智能交通和智慧城市系统的出现，如何通过计算机视觉对多摄像头监控视频中的海量车辆轨迹进行高效合理的聚类分析已成为一个重要的研究领域。传统的轨迹聚类算法没有考虑摄像头位置和视野，忽略了摄像头与场景之间视频对象运动的层次关系，导致多摄像头视频对象轨迹聚类效果不佳。为应对这一挑战，本文提出了一种基于时空分组的多摄像头车辆轨迹层次聚类算法。首先，我们根据不等长轨迹的最佳点对应规则对摄像头组内的车辆轨迹进行监督聚类。然后，提取每组下视频对象的起始点和终点，根据跨摄像头组的数量对轨迹进行层次化，并对不同层次的子段集进行监督聚类。该方法考虑了摄像头与视频场景之间的空间关系，而传统算法并未考虑这一点。通过比较轮廓系数和CPU时间的实验，证明了该方法的有效性。
