Road Scene Analysis by Stereovision: a Robust and Quasi-Dense Approach

A stereovision method is presented in this paper, to compute reliable and quasi-dense disparity maps of road scenes using in-vehicle cameras. It combines the advantages of the "v-disparity" approach and a quasi-dense matching algorithm. In this aim, road surface and vertical planes of the scene are first extracted using the sparse "v-disparity" approach. The knowledge of these global surfaces of the scene is then used to guide a quasi-dense matching algorithm and to propagate disparity information on horizontal edges. Both algorithms are presented and compared. Then, our approach is presented and examples of quasi-dense disparity maps are given. Finally, the efficiency of the method is illustrated by the accurate positioning of a bounding box around a vehicle in a bad contrasted video sequence