Real-Time Head Tracker Using Color, Stereovision and Ellipse Fitting in a Particle Filter

This paper proposes the use of a particle filter combined with color, depth information and shape features as an efficient and effective way to deal with tracking a head on the basis of image stream coming from a mobile stereovision camera. The head is modeled in the 2D image domain by an ellipse. The color distribution within interior of the ellipse is represented by a color histogram. The color histogram is dynamically updated over time. The length of the ellipse’s minor axis is determined on the basis of depth information. The particles representing the candidate ellipses are weighted in each time step in respect of intensity gradient near the edge of the ellipse and matching score of the color histograms representing the interior of an ellipse surrounding the tracked object and currently analyzed one. The proposed algorithm can track a head reliably in case of temporal occlusions as well as varying illumination conditions by dealing with multiple hypotheses for the pose. Experimental results obtained on long image sequences show the feasibility of our approach to perform tracking a head undergoing complex changes of shape and appearance against a varying background. The tracker has been evaluated in experiments consisting in face tracking with a real mobile agent.