Multiple-Hand-Gesture Tracking using Multiple Cameras

Akira Utsumi, Jun Ohya

Abstract: We propose a method of tracking 3D position, posture, and shapes of human hands from multiple-viewpoint images. Self-occlusion and hand-hand occlusion are serious problems in the vision-based hand tracking. Our system employs multiple-viewpoint and viewpoint selection mechanism to reduce these problems. Each hand position is tracked with a Kalman filler and the motion vectors are updated with image features in selected images that do not include hand-hand occlusion. 3D hand postures are estimated with a small number of reliable image features. These features are extracted based on distance transformation, and they are robust against changes in hand shape and self-occlusion. Finally, a "best view" image is selected for each hand for shape recognition. The shape recognition process is based on a Fourier descriptor. Our system can be used as a user interface device an a virtual environment, replacing glove-type devices and overcoming most of the disadvantages of contact-type devices.