Tracking and segmenting people in varying lighting conditions using colour

Abstract

Colour cues were used to obtain robust detection and tracking of people in relatively unconstrained dynamic scenes. Gaussian mixture models were used to estimate probability densities of colour for skin, clothing and background. These models were used to detect, track and segment people, faces and hands. A technique for dynamically updating the models to accommodate changes in apparent colour due to varying lighting conditions was used. Two applications are highlighted: (1) actor segmentation for virtual studios, and (2) focus of attention for face and gesture recognition systems. A system implemented on a 200 MHz PC tracks multiple objects in real time.