{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/railsem19-a-dataset-for-semantic-rail-scene","title":"RailSem19: A Dataset for Semantic Rail Scene Understanding","arxiv_id":null,"date":"2019-06-16","proceeding":"CVPR 2019 6","authors":["Oliver Zendel","Markus Murschitz","Marcel Zeilinger","Daniel Steininger","Sara Abbasi","Csaba Beleznai"],"abstract":"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","url_abs":"https://openaccess.thecvf.com/content_CVPRW_2019/html/Autonomous_Driving/Zendel_RailSem19_A_Dataset_for_Semantic_Rail_Scene_Understanding_CVPRW_2019_paper.html","url_pdf":"https://openaccess.thecvf.com/content_CVPRW_2019/papers/WAD/Zendel_RailSem19_A_Dataset_for_Semantic_Rail_Scene_Understanding_CVPRW_2019_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[{"slug":"railsem19","name":"RailSem19","full_name":"RailSem19: A Dataset for Semantic Rail Scene Understanding"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}