Head tracking using stereo

Abstract.

Head tracking is an important primitive for smart environments and perceptual user interfaces where the poses and movements of body parts need to be determined. Most previous solutions to this problem are based on intensity images and, as a result, suffer from a host of problems including sensitivity to background clutter and lighting variations. Our approach avoids these pitfalls by using stereo depth data together with a simple human-torso model to create a head-tracking system that is both fast and robust. We use stereo data (Commercial equipment and materials are identified in order to adequately specify certain procedures. In no case does such identification imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.) to derive a depth model of the background that is then employed to provide accurate foreground segmentation. We then use directed local edge detectors on the foreground to find occluding edges that are used as features to fit to a torso model. Once we have the model parameters, the location and orientation of the head can be easily estimated. A useful side effect from using stereo data is the ability to track head movement through a room in three dimensions. Experimental results on real image sequences are given.