A Two-Step Minimization Algorithm For Model-Based Hand Tracking

Abstract

Model-based methods to the tracking of an articulated hand in a video sequence could be divided in two categories. The first one, called stochastic methods, uses stochastic filters such as kalman or particle ones. The second category, named deterministic methods, defines a dissimilarity function to measure how well the hand model is aligned with the hand images of a video sequence. This dissimilarity function is then minimized to achieve the hand tracking. Two well-known problems are related to the minimization algorithms. The first one is that of local minima. The second problem is that of computing time required to reach the solution. These problems are compounded with the large number of degrees of freedom (DOF) of the hand (around 26). The choice of the function to be minimized and that of the minimization process can be an answer to these problems. In this paper two major contributions are presented. The first one defines a new dissimilarity function, which gives better results for hand tracking than other well-known functions like the directed chamfer or hausdorff distances. The second contribution proposes a minimization process that operates in two steps. The first one provides the global parameters of the hand, i.e. position and orientation of the palm, whereas the second step gives the local parameters of the hand, i.e. finger joint angles. Operating in two stages, the proposed two-step algorithm reduces the complexity of the minimization problem. Indeed, it seems more robust to local minima than a one-step algorithm and improves the computing time needed to get the desired solution.