Random-Walker Monocular Road Detection in Adverse Conditions Using Automated Spatiotemporal Seed Selection

A key module of modern advanced driver-assistance systems (ADASs) is the road detector, which has to be robust, even under adverse conditions. The ultimate goal of such a system, which uses only visual information acquired from a color video camera, is to classify each frame pixel as belonging to the road or not. In this direction, this paper proposes a new fully automatic algorithm that combines both time and spatial information using the efficient random-walker algorithm (RWA) as a segmentation tool. A novel technique for automatic seed selection is proposed, utilizing features derived from a shadow-resistant optical flow estimator using the $c_{1}$ channel of the $c_{1}c_{2}c_{3}$ color space, along with a priori information and previous frame segmentation results. The proposed system is qualitatively assessed using video sequences in both typical and adverse conditions, including heavy traffic, shadows, tunnels, rain, night, etc. It is also quantitatively compared with previous efforts on a publicly available manually annotated onboard video database, providing superior results.