Sequential Monte Carlo tracking of body parameters in a sub-space

In recent years sequential Monte Carlo (SMC) methods have been applied to handle some of the problems inherent to model-based tracking. Two issues regarding SMC are investigated in the context of estimating the 3D pose of the human arm. Firstly, we investigate how to apply a subspace to representing the pose of a human arm more efficiently, i.e., reducing the dimensionality. Secondly, we investigate how to apply a local method to estimated the maximum a posteriori (MAP). The former issue is based on combining a screw axis representation with the position of the hand in the image. The latter issue is handled by applying a method based on maximising a proximity function, to estimate the MAP. We find that both the subspace and the proximity function are sound strategies and that they are an improvement over the current SMC-methods.