Dynamical Pose Filtering for Mixtures of Gaussian Processes

In this paper we propose a novel method for discriminative monocular human pose tracking using a mixture of Gaussian processes and a dynamic programming algorithm for selecting the optimal expert at each frame. The proposed tracking mechanism incorporates a dynamical model into the predictive distribution which is combined with the appearance model in a principled manner. This model is able to give a smoother predicted pose and resolves ambiguities in the image to pose mapping. We introduce a mixture of Gaussian processes model which optimises the size and location of each expert ensuring that each expert models a coherent region of the dataset resulting in an accurate predictive density. We compare our method to other state of the art methods on 2D and 3D monocular pose estimation on ballet and sign language data sets.