Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPRW.2019.00161 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | ISSN |
Computer vision,Computer science,Artificial intelligence | Conference | 2160-7508 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliver Zendel | 1 | 4 | 1.09 |
| Markus Murschitz | 2 | 9 | 2.91 |
| Marcel Zeilinger | 3 | 0 | 0.34 |
| Daniel Steininger | 4 | 0 | 2.03 |
| Sara Abbasi | 5 | 0 | 0.34 |
| Csaba Beleznai | 6 | 367 | 18.96 |