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SynPeDS: A Synthetic Dataset for Pedestrian Detection in Urban Traffic Scenes

Thomas Stauner; Frédérik Blank; David Michael Fürst; Johannes Günther; Korbinian Hagn; Philipp Heidenreich; Markus Huber; Bastian Knerr; Thomas Schulik; Karl-Ferdinand Leiß
In: Proceedings of the 6th ACM Computer Science in Cars Symposium. ACM Computer Science in Cars Symposium (CSCS-2022), ACM, 12/2022.


We introduce the Synthetic Pedestrian Dataset (SynPeDS) which was designed to support a systematic safety analysis for pedestrian detection tasks in urban scenes. The dataset was generated synthetically with a real-time and a physically-based rendering pipeline and provides camera frames and in part associated LiDAR point clouds. It contains ground truth for semantic segmentation, instance segmentation, 2D and 3D bounding boxes, and in part, pose information and bodypart segmentation. In particular, it comes with a large amount of meta information for in-depth performance and safety analysis, e.g. addressing semantic properties of the pedestrians and their environment in the frames. Some scenarios were specifically designed to systematically cover certain safety-relevant or performance-reducing dimensions of the input space, defined in project KI Absicherung. The dataset does not claim to be complete or free of bias, but to support coverage and data distribution studies.


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