Color correction for object tracking across multiple cameras

Color is a powerful attribute that is used to characterize objects for tracking and other surveillance tasks. Since color is dependent on ambient illumination and the imaging equipment, the reliability of color features decreases as the object moves through regions observed with different cameras and with different illumination conditions. Typically, this issue is addressed using alternative color spaces or highly simplified color transformation. In this paper, we propose a more methodical approach to achieving color consistency using colorimetric principles. We model the image analysis system as an observer and develop camera-specific transformations so that images of the same object appear similar to this observer. The transformations thus developed are approximated with 3D look-up tables for fast operation. Experimental results show improved color consistency even under two very different illumination conditions. The results are evaluated both qualitatively and in terms of the effect on a particle filter based object tracker.