Color Recognition by Learning: ATR in Color Images

Traditional methods for ATR (Automatic Target Recognition) use infrared (IR) sensors for detecting heat emanating from targets. IR-based ATR techniques are susceptible to sensor-induced errors; for instance, targets may not be detected if they are cold (when vehicle engines are turned o ), or when the background is hot (on a hot day). This work presents an approach to real-time color-based ATR which uses multivariate decision trees for recursive non-parametric function approximation to learn the color of a target from training samples, and then detects targets by classifying pixels based on the approximated function. Tests of the color-based system, sanctioned by the U.S. Defense Advanced Research Projects Agency Unmanned Ground Vehicle Project (DARPA-UGV), have resulted in a 90% target detection rate (compared to the 45% detection rate of the IR-based system developed for the same tests). When the color system was used in conjunction with the IR-based system, 100% of the targets were detected.