Detection and Estimation of Pointing Gestures in Dense Disparity Maps

In this paper we describe a real-time system for detecting pointing gestures and estimating the direction of pointing using stereo cameras. Previously, similar systems where implemented using color-based blob trackers, which relied on effective skin color detection; this approach is sensitive to lighting changes and the clothing worn by the user. In contrast, we used a stereo system that produces dense disparity maps in real-time. Disparity maps are considerably less sensitive to lighting changes. Our system subtracts the background, analyzes the foreground pixels to break the body into parts using a robust mixture model, and estimates the direction of pointing. We have tested the system on selecting the targets in a room and controlling the cursor on a wall screen.