3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis
We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic (PLSA) models to describe the mapping of image features to 3D human pose estimates. PLSA has been successfully used to model the co-occurrence of dyadic data on problems such as image annotation where image features are mapped to word categories via latent variable semantics. We apply the PLSA approach to motion tracking by extending it to a sequential setting where the latent variables describe intrinsic motion semantics linking human figure appearance to 3D pose estimates. This dynamic PLSA (DPLSA) approach is in contrast to many current methods that directly learn the often high-dimensional image-to-pose mappings and utilize subspace projections as a constraint on the pose space alone. As a consequence, such mappings may often exhibit increased computational complexity and insufficient generalization performance. We demonstrate the utility of the proposed model on the synthetic dataset and the task of 3D human motion tracking in monocular image sequences with arbitrary camera views. Our experiments show that the proposed approach can produce accurate pose estimates at a fraction of the computational cost of alternative subspace tracking methods.