Articulated hand tracking using key poses driven particle filtering

Tracking an articulated hand is very difficult problem due to the high dimensionality of the hand joint movements. We propose a system to track the articulated hand using our key pose driven particle filtering. The articulated hand is modeled as a cardboard model which has 24 DOF. Using motion constraints between the finger joints, the dimension of the articulated hand model is reduced to 13 DOF. The proposal distribution is based on the Gibbs sampler-based motion model and the matching probabilities of key poses onto the observation image. The motion model is based on the motion constraints between the hand joints. Each movement of joints is predicted by Gibbs sampler which is modeled as our motion model. We show the experimental results of tracking the articulated hand by Key poses driven particle filtering.