Real-Time Multi-Camera System for on-Board Train Path Estimation

As the need for more efficient, safe and environmentally friendly transportation increases, the implementation of ADAS systems on trains is becoming essential. In this work, we propose a flexible pipeline for accurately determining the position of the ego-track in railway scenarios. This pipeline is capable of detecting and classifying switches in the track, and use that information to determine which of both branches is part of the ego-track. We evaluate this pipeline on RGB cameras using the OSDaR23 dataset and achieve a Train Centerline Offset Metric (TCOM) of under 15cm until a distance of 75m. We also demonstrate the flexibility of the pipeline by combining the information of wide-angle and zoom camera images, which provides an improvement in TCOM of around 15cm in distances longer than 100m.