Style adaptive Bayesian tracking using explicit manifold learning

Characteristics of the 2D contour shape deformation in human motion contain rich information and can be useful for human identification, gender classification, 3D pose reconstruction and so on. In this paper we introduce a new approach for contour tracking for human motion using an explicit modeling of the motion manifold and learning a decomposable generative model. We use nonlinear dimensionality reduction to embed the motion manifold in a low dimensional configuration space utilizing the constraints imposed by the human motion. Given such embedding, we learn an explicit representation of the manifold, which reduces the problem to a one-dimensional tracking problem and also facilitates linear dynamics on the manifold. We also utilize a generative model through learning a nonlinear mapping between the embedding space and the visual input space, which facilitates capturing global deformation characteristics. The contour tracking problem is formulated as states estimation in the decomposed generative model parameter within a Bayesian tracking framework. The result is a robust, adaptive gait tracking with shape style estimation.