A histogram-based Joint Boosting classification for determining urban road

Road detection for inner-city scenarios remains a difficult problem due to the high complexity in scene layout with unmarked or weakly marked roads and poor lighting conditions. This paper introduces a novel method based on multi normalized-histogram with Joint Boosting algorithm to road recognition. The approach performs three modules in parallel that are the Image Segmentation, the Texton Maps and the new one Dispton Maps. The first one applies a combination of pre-filters with Watershed Transform to make the super-pixel. The last two perform a dense feature extraction based on 2D texture image and 3D disparity image to get appearance, shape and context information. At last, a discriminative model of road class is learned based on distribution of Textons and Disptons applied in Joint Boosting algorithm. The proposed work reports real experiments carried out in a challenging urban environment utilizing the modern KITTI benchmark for road areas in which meaningful evaluation can be done to illustrate the validity and application of this approach.