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 to derive a depth model of the background which is then employed to provide accurate foreground segmentation. We then use directed local edge detectors on the foreground to find occluding edges which 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.