Papers › RailSem19: A Dataset for Semantic Rail Scene Understanding
RailSem19: A Dataset for Semantic Rail Scene Understanding
Oliver Zendel, Markus Murschitz, Marcel Zeilinger, Daniel Steininger, Sara Abbasi, Csaba Beleznai
Solving tasks for autonomous road vehicles using com-puter vision is a dynamic and active research field. How-ever, one aspect of autonomous transportation has receivedlittle contributions: the rail domain. In this paper, we intro-duce the first public dataset for semantic scene understand-ing for trains and trams: RailSem19. This dataset consistsof 8500 annotated short sequences from the ego-perspectiveof trains, including over 1000 examples with railway cross-ings and 1200 tram scenes. Since it is the first image datasettargeting the rail domain, a novel label policy has been de-signed from scratch. It focuses on rail-specific labels notcovered by any other datasets. In addition to manual an-notations in the form of geometric shapes, we also supplydense pixel-wise semantic labeling. The dense labeling isa semantic-aware combination of (a) the geometric shapesand (b) weakly supervised annotations generated by exist-ing semantic segmentation networks from the road domain.Finally, multiple experiments give a first impression on howthe new dataset can be used to improve semantic sceneunderstanding in the rail environment. We present proto-types for the image-based classification of trains, switches,switch states, platforms, buffer stops, rail traffic signs andrail traffic lights. Applying transfer learning, we presentan early prototype for pixel-wise semantic segmentation onrail scenes. The resulting predictions show that this newdata also significantly improves scene understanding in sit-uations where cars and trains interact
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections