Vision-based road detection using road models
Vision-based road detection is very challenging since the road is in an outdoor scenario imaged from a mobile platform. In this paper, a new top-down road detection algorithm is proposed. The method is based on scene (road) classification which provides the probability that an image contains certain type of road geometry (straight, left/right curve, etc.). During the training of the classifier a road probability map is also learned for each road geometry. Then, the proper pixel-based method is selected and fused to provide an improved road detection approach. From experiments it is concluded that the proposed method outperforms state-of-the-art algorithms in a frame by frame context.