Color Vision For Road Following

Jill D. Crisman; Charles E. Thorpe

doi:10.1117/12.949096

10 March 1989 Color Vision For Road Following

Jill D. Crisman, Charles E. Thorpe

Proceedings Volume 1007, Mobile Robots III; (1989) https://doi.org/10.1117/12.949096
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States

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.

Citation Download Citation

Jill D. Crisman and Charles E. Thorpe "Color Vision For Road Following", Proc. SPIE 1007, Mobile Robots III, (10 March 1989); https://doi.org/10.1117/12.949096

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