SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras

Intelligent transportation systems (ITS) have revolutionized modern road infrastructure, providing essential functionalities such as traffic monitoring, road safety assessment, congestion reduction, and law enforcement. Effective vehicle detection and accurate vehicle pose estimation are crucial for ITS, particularly using monocular cameras installed on the road infrastructure. One fundamental challenge in vision-based vehicle monitoring is keypoint detection, which involves identifying and localizing specific points on vehicles (such as headlights, wheels, taillights, etc.). However, this task is complicated by vehicle model and shape variations, occlusion, weather, and lighting conditions. Furthermore, existing traffic perception datasets for keypoint detection predominantly focus on frontal views from ego vehicle-mounted sensors, limiting their usability in traffic monitoring. To address these issues, we propose SKoPe3D, a unique synthetic vehicle keypoint dataset generated using the CARLA simulator from a roadside perspective. This comprehensive dataset includes generated images with bounding boxes, tracking IDs, and 33 keypoints for each vehicle. Spanning over 25k images across 28 scenes, SKoPe3D contains over 150k vehicle instances and 4.9 million keypoints. To demonstrate its utility, we trained a keypoint R-CNN model on our dataset as a baseline and conducted a thorough evaluation. Our experiments highlight the dataset's applicability and the potential for knowledge transfer between synthetic and real-world data. By leveraging the SKoPe3D dataset, researchers and practitioners can overcome the limitations of existing datasets, enabling advancements in vehicle keypoint detection for ITS.