Color Vision for Road Following

Abstract

At Carnegie Mellon University, we have two new vision systems for outdoor road following. The first system, called SCARF (Supervised Classification Applied to Road Following), is designed to be fast and robust when the vehicle is running in both sunshine and shadows under constant illumination. The second system, UNSCARF (UNSupervised Classification Applied to Road Following), is slower, but provides good results even if the sun is alternately covered by clouds or uncovered. SCARF incorporates our results from our previous experience with road tracking by supervised classification. It is an adaptive supervised classification scheme using color data from two cameras to form a new six dimensional color space. The road is localized by a Hough space technique. SCARF is specifically designed for fast implementation on the WARP supercomputer, an experimental parallel architecture developed at Carnegie Mellon.

UNSCARF uses an unsupervised classification algorithm to group the pixels in the image into regions. The road is detected by finding the set of regions which, grouped together, best match the road shape. UNSCARF can be expanded easily to perform unsupervised classification on any number of features, and to use any combination of constraints to select the best combination of regions. The basic unsupervised classification segmentation will also have applications outside the realm of road following.