Traditional methods for ATR Automatic Target Recognition use infrared IR sensors for detecting heat emanating fromtargets. 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 wer...
Shashi D. Buluswar, Bruce A. Draper