Abstract
This paper proposes a new method for detecting a road from a stereo pair of images. First, the horizon is accurately estimated by a robust, weighted-sampling RANSAC-like method in the improved v-disparity map. The vanishing point of the road region is located using both the horizon information and road flatness constraints. Then it is used as the source node of a weighted graph formed by the pixels of the left stereo-image and their adjacency relationships. The weight of each edge measures the inconsistency of adjacent pixels, and is computed using both the gray-scale and disparity information. Detecting road borders is thus reduced to finding two shortest paths from the source node to the bottom row of the image by the Dijkstra algorithm. The proposed method has been tested on 2621 image pairs of different road scenes from the KITTI dataset. Our experiments demonstrate that this training free approach detects horizon, vanishing point, and road region accurately and robustly, and compares favorably with the state of the art on the KITTI benchmark.
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