Vision-based railroad track extraction using dynamic programming

Most of the common driver assistant systems for detection of obstacles work on unstructured environments. These environments generally include many non-planar surfaces which pose a big challenge for vision systems. Similar problems exist for railroad environments which often contain complex shapes and surfaces like hills and vegetation along railroad tracks. In railroad transportation, the main task of a train driver is to carefully focus on the track. Therefore the field of view of a train driver must contain the space between two rails in front of the train and the near lateral area (left and right side) of these rails. In this paper, we present an algorithm to extract the train course and railroad track space in front of the train using dynamic programming in railroad environments. We use dynamic programming to compute the optimal path which gives the minimum cost to extract the railroad track space. The proposed algorithm extracts the left and right rails using dynamic programming simultaneously. Our method does not need any static calibration process. For this purpose, a camera system was installed in front of a locomotive. Experimental results show the effectiveness of the algorithm.