Data-driven road detection

In this paper, we tackle the problem of road detection from RGB images. In particular, we follow a data-driven approach to segmenting the road pixels in an image. To this end, we introduce two road detection methods: A top-down approach that builds an image-level road prior based on the traffic pattern observed in an input image, and a bottom-up technique that estimates the probability that an image superpixel belongs to the road surface in a nonparametric manner. Both our algorithms work on the principle of label transfer in the sense that the road prior is directly constructed from the ground-truth segmentations of training images. Our experimental evaluation on four different datasets shows that this approach outperforms existing top-down and bottom-up techniques, and is key to the robustness of road detection algorithms to the dataset bias.