Adaptive color space switching for tracking under varying illumination

Many studies use color space models (CSM) and color distribution models (CDM) for detection of faces in an image. We develop a procedure that adaptively switches CSMs throughout the processing of a video. We show that this works in environments with varying types of illumination. In addition, a new performance measure for evaluating tracking algorithms is proffered. Extensive testing of the procedure found that switching between the color spaces resulted in increased tracking performance when compared to using single CSMs throughout. The methodology developed can be used to find the optimal CSM-CDM combination in the design of adaptive color tracking systems. The adaptive color space switching algorithm has linear computational time complexity O(S), at each iteration, where S is the picture size in pixels.