Riehl Kevin, El-Baklish Shaimaa K, Kouvelas Anastasios, Makridis Michail A
Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland.
Sci Rep. 2025 Jul 28;15(1):27522. doi: 10.1038/s41598-025-12301-2.
Vehicle trajectories offer valuable insights for a wide range of road transportation applications. Due to the rise of drone technology, a growing branch of literature explores optical vehicle trajectory extraction from aerial videos, where object detection using neural networks is an important component. Horizontal bounding box object detection struggles to differentiate well between rotated vehicles, especially when dealing with complex backgrounds or densely packed vehicles. This work proposes a generalizable computation pipeline that leverages angular information to extract high-quality trajectories starting from video recordings and ending in trajectories in Cartesian and lane coordinates. A trajectory reconstruction algorithm is designed to be vehicle- and driver-informed and to maximize the physical consistency of the reconstructed trajectories both on the individual vehicles' and platoon levels. A comprehensive benchmark of 18 object detection models on a real-world video dataset demonstrates how oriented object detection and the use of angular information can be used to significantly improve the consistency of extracted trajectories (15% better internal, and 20% better platoon consistency), and that orientation-informed trajectories can be reconstructed to lane coordinates of higher quality. The reconstructed vehicle trajectories better capture car-following and traffic dynamics, thereby improving their usability for traffic flow studies.
车辆轨迹为广泛的道路运输应用提供了有价值的见解。由于无人机技术的兴起，越来越多的文献分支探索从航拍视频中提取光学车辆轨迹，其中使用神经网络进行目标检测是一个重要组成部分。水平边界框目标检测难以很好地区分旋转的车辆，尤其是在处理复杂背景或密集车辆时。这项工作提出了一种可推广的计算管道，该管道利用角度信息从视频记录开始提取高质量轨迹，并以笛卡尔坐标和车道坐标中的轨迹结束。设计了一种轨迹重建算法，该算法以车辆和驾驶员为依据，并在单个车辆和车队层面上最大化重建轨迹的物理一致性。在一个真实世界视频数据集上对18个目标检测模型进行的全面基准测试表明，定向目标检测和角度信息的使用如何能够显著提高提取轨迹的一致性（内部一致性提高15%，车队一致性提高20%），并且基于方向信息的轨迹可以重建为更高质量的车道坐标。重建后的车辆轨迹更好地捕捉了跟车和交通动态，从而提高了它们在交通流研究中的可用性。
