Color Recognition by Learning: ATR in Color Images

Shashi D. Buluswar and Bruce A. Draper
Dept. of Computer Science
University of Massachusetts
Amherst, MA
U.S.A.
[buluswar,bdraper]@cs.umass.edu

Abstract:

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 off), 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.

• 1 Introduction
• 2 IR-based ATR
• 3 Color imagery for ATR
• 4 Multivariate Decision Trees (MDT) for learning target color
• 5 ATR system using MDT
• 6 Results
• 7 Conclusions
• References