Adaptive Colour Calibration for Object Tracking under Spatially-Varying Illumination Environments

In the context of a Fuzzy-Genetic system, auto-calibration of colour classifiers, under spatially varying illumination conditions, to produce near perfect object recognition accuracy requires a balancing act for the fitness function. One general approach would be to maximise the true positives while minimising the false positives. This has been found effective in the presence of large amount of noise. However, experiments show that this fitness function needs improvement for cases where there are target colours with similar hues. In this paper, we present an extension to our fuzzy-genetic colour contrast fusion algorithm, now utilising a fitness function that detects clusters of false positives, and limits the search space for finding the properties of the colour classifier. We tested the performance of the auto-calibrated colour classifiers by subjecting them to object recognition tasks in the robot soccer domain, under varying illumination conditions, until we find its limits. It was observed that the accuracy of the object recognition began to degrade, on the average, at illumination settings that are either about three times brighter (starting from 797.4 lux), or two times darker (less than 138 lux) than what it was trained for (average of 285.47 lux). Otherwise, near perfect recognition accuracy is achieved.