OpenVTER: An Open Vehicle Trajectory Extraction Framework Based on Rotated Bounding Boxes

Vehicle trajectory data is essential for analyzing and modeling complex traffic behaviors. Although extraction of vehicle trajectory from aerial video data is not a new problem, obtaining trajectories with heading information across various road types, such as intersections or long road segments, requires further research. In this paper, we propose OpenVTER, a generalized Open-source Vehicle Trajectory Extraction framework based on Rotated bounding boxes (RBBs). This framework includes several key components: video stabilization, image division, vehicle detection, vehicle tracking, and data post-processing. Specifically, the rotated vehicle detection model, named YOLOX-R, is applied to detect the small and rotated vehicles using RBBs that provide vehicle heading information. A base-frame video stabilization method is proposed to reduce error accumulation in the transformation matrix and improve the computational efficiency. The rotated vehicle tracking model, named SORT-R, is proposed to enable real-time tracking of RBBs. The performance of YOLOX-R is evaluated on two datasets, showing that vehicle detection challenges are well addressed. Ablation experiments were also conducted to analyze the effectiveness of different modules. Subsequently, we evaluate the completeness of the extracted trajectories under various road types and lighting conditions. The extracted trajectories are also compared with the NGSIM dataset, focusing on internal and platoon consistency. These evaluations demonstrate both the effectiveness and practicality of the proposed framework. Additionally, the visualization analyses of different road types demonstrate the advantages of the trajectories extracted by OpenVTER in various road scenarios for traffic research. The code and dataset are available online for non-commercial research purposes.