RailSem19: A Dataset for Semantic Rail Scene Understanding

Oliver Zendel, Markus Murschitz, Marcel Zeilinger, Daniel Steininger, Sara Abbasi, Csaba Beleznai; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Solving tasks for autonomous road vehicles using computer vision is a dynamic and active research field. However, one aspect of autonomous transportation has received little contributions: the rail domain. In this paper, we introduce the first public dataset for semantic scene understanding for trains and trams: RailSem19. This dataset consists of 8500 annotated short sequences from the ego-perspective of trains, including over 1000 examples with railway crossings and 1200 tram scenes. Since it is the first image dataset targeting the rail domain, a novel label policy has been designed from scratch. It focuses on rail-specific labels not covered by any other datasets. In addition to manual annotations in the form of geometric shapes, we also supply dense pixel-wise semantic labeling. The dense labeling is a semantic-aware combination of (a) the geometric shapes and (b) weakly supervised annotations generated by existing semantic segmentation networks from the road domain. Finally, multiple experiments give a first impression on how the new dataset can be used to improve semantic scene understanding in the rail environment. We present prototypes for the image-based classification of trains, switches, switch states, platforms, buffer stops, rail traffic signs and rail traffic lights. Applying transfer learning, we present an early prototype for pixel-wise semantic segmentation on rail scenes. The resulting predictions show that this new data also significantly improves scene understanding in situations where cars and trains interact.