Stereovision-Based Head Tracking Using Color and Ellipse Fitting in a Particle Filter

This paper proposes the use of a particle filter combined with color, depth information, gradient and shape features as an efficient and effective way of dealing with tracking of 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. A weighting function is used to include spatial information in color histogram representing the interior of the ellipse. The lengths of the ellipse’s minor axis are determined on the basis of depth information. The dissimilarity between the current model of the tracked object and target candidates is indicated by a metric based on Bhattacharyya coefficient. Variations of the color representation as a consequence of ellipse’s size change are handled by taking advantage of the scale invariance of the similarity measure. The color histogram and parameters of the ellipse are dynamically updated over time to discriminate in the next iteration between the candidate and actual head representation. This makes possible to track not only a face profile which has been shot during initialization of the tracker but in addition different profiles of the face as well as the head can be tracked. Experimental results which were obtained on long image sequences in a typical office environment show the feasibility of our approach to perform tracking of a head undergoing complex changes of shape and appearance against a varying background. The resulting system runs in real-time on a standard laptop computer installed on a real mobile agent.