Video based system for railroad collision warning

Abstract

Autonomous systems can assist humans in the important task of safe driving. Such systems can warn people about possible risks, take actions to avoid accidents or guide the vehicle without human supervision. In railway scenarios a camera in front of the train can aid drivers with the identification of obstacles or strange objects that can pose danger to the route. Image processing in these applications is not easy of performing. The changing conditions create scenes where background is hard to detect, lighting varies and process speed must be fast. This article describes a first approximation to the solution of the problem where two complementary approaches are followed for detecting and tracking obstacles on videos captured from a train driver perspective. The first strategy is a simple-frame-based approach where every video frame is analyzed using the Hough transform for detecting the rails. On every rail a systematic search is done detecting obstacles that can be dangerous for the train course. The second approach uses consecutive frames for detecting the trajectory of moving objects. Analyzing the sparse optical flow the candidate objects are tracked and their trajectories computed in order to determine their possible route to collision. For testing the system we have used videos where preselected fixed and moving obstacles have been superimposed using the Chroma key effect. The system had shown a real time performance in detecting and tracking the objects. Future work includes the test of the system on real scenarios and the validation over changing weather conditions.