Model-based hand pose estimation using multiple viewpoint silhouette images and Unscented Kalman Filter

This paper addresses pose estimation of a hand in motion through a model-based vision-based system. Previous research on human hand tracking and pose estimation usually suffers from limited number of degrees-of-freedom estimated, camera orientation (i.e., the hand is restricted to a particular pose while moving) and occlusion. We describe and evaluate a system that allows several DOF of the hand to be estimated without hindering its motion and while minimizing occlusion. To allow for a complete 3D motion, a voxel model and a skeletal model of the hand are used. The system uses multiple viewpoint cameras to obtain information of the hand motion. Due to the non-linear characteristics of the system, unscented Kalman filter (UKF) is used to track the hand motion. UKF estimates the hand pose by minimizing the difference between the skeletal model and the voxel model. Estimation results from different hand motions of up to 15DOF show the feasibility of the system.