Robust road detection from a single image

Road detection from images is a challenging task in computer vision. Previous methods are not robust, because their features and classifiers cannot adapt to different circumstances. To overcome this problem, we propose to apply unsupervised feature learning for road detection. Specifically, we develop an improved encoding function and add a feature selection process to obtain robust and discriminative road features. Besides, a road segmentation algorithm is proposed to extract road regions from the learned feature maps, in which a tree structure is established to represent the hierarchical relations of various regions segmented by multiple thresholds, and a two-loop optimization is then employed to select the most stable regions as road areas. Experimental results on several challenging datasets justify the effectiveness of our method.