Robust Tramway Detection in Challenging Urban Rail Transit Scenes

With the rapid development of light rail transit, tramway detection based on video analysis is becoming the prerequisite and necessary task in driver assistance system. The system should be capable of automatically detecting the trackway using on-board camera in order to determine the train driving limit. However, due to the diversification of ground types, the diversity of weather conditions and the differences in illumination situations, this goal is very challenging. This paper presents a real-time tramway detection method that can effectively deal with various challenging scenarios in the real world of urban rail transit environment. It first uses an adaptive multi-level threshold to segment the ROI of the trolley track, where the local cumulative histogram model is used to estimate the threshold parameters. And then use the regional growth method to reduce the impact of environmental noise and predict the trend of tramway. We have experimentally proved that the method can correctly detect the tramway even in many undesirable situations and use less computational time to meet real-time requirements.