Generalizable Vision-Based Framework for Vehicle Trajectory Estimation and Conflict Analysis at Intersections

The lack of scalable and cost-effective methods for extracting actionable vehicle trajectories from existing traffic closed-circuit television (CCTV) infrastructure limits proactive traffic safety analysis. Traditional trajectory estimation approaches often rely on LiDAR, radar, or calibrated camera systems, which are costly and lack scalability. This study introduces a novel, plug-and-play framework for vision-based vehicle trajectory estimation using monocular CCTV footage that is generalizable across locations. The proposed system combines homography-based birds eye view transformation with a You Only Look Once (YOLO)v8-oriented bounding box (OBB) detection to estimate vehicle trajectories from traffic footage trained on a custom dataset. The framework introduces a novel custom-defined “space” bounding box that accurately captures the physical footprint of moving objects. It leverages visual cues, such as tire shadows and distortion patterns, effectively addressing challenges related to occlusion and distortions. The YOLOv8-OBB model, trained on the compiled dataset, achieves high performance with mean average precision @50–95 of 0.92, precision and recall exceeding 0.95. Trajectory refinement was achieved through temporal sub-sampling, moving average smoothing, and slope-based orientation correction, resulting in stable and physically realistic paths even during turns and visual occlusions. Calculated speed and acceleration profiles from refined trajectories align with real-world driving behavior, further validating the system’s accuracy. The pipeline was successfully tested on an unseen intersection, demonstrating its generalizability across varied traffic geometries and perspectives. This work presents a scalable solution for trajectory-based traffic monitoring, with potential applications in conflict detection, traffic modeling, and intersection safety assessments using widely available surveillance infrastructure.