Color machine vision for autonomous vehicles

Color can be a useful feature in autonomous vehicle systems that are based on machine vision, for tasks such as obstacle detection, lane/road following, and recognition of miscellaneous scene objects. Unfortunately, few existing autonomous vehicle systems use color to its full extent, largely because color-based recognition in outdoor scenes is complicated, and existing color machine vision techniques have not been shown to be e ective in realistic outdoor images. This paper presents a technique for achieving e ective real-time color recognition in outdoor scenes. The technique uses Multivariate Decision Trees for piecewise linear non-parametric function approximation to learn the color of a target object from training samples, and then detects targets by classifying pixels based on the approximated function. The method has been successfully tested in several domains, such as autonomous highway navigation, o -road navigation and target detection for unmanned military vehicles, in projects such as the U.S. National Automated Highway System (AHS) and the U.S. Defense Advanced Project Agency Unmanned Ground Vehicle (DARPA-UGV). MDT-based systems have been used in stand-alone mode, as well as in conjunction with systems based on other sensor con gurations. Supported by the Advanced Research Projects Agency through Rome Labs under contract F30602-94-C0042.