Discriminative Learning of Dynamical Systems for Motion Tracking

Abstract

We introduce novel discriminative learning algorithms for dynamical systems. Models such as conditional random fields or maximum entropy Markov models outperform the generative hidden Markov models in sequence tagging problems in discrete domains. However, continuous state domains introduce a set of constraints that can prevent direct application of these traditional models. Instead, we suggest to learn generative dynamic models with discriminative cost functionals. For linear dynamical systems, the proposed methods provide significantly lower prediction error than the standard maximum likelihood estimator, often comparable to nonlinear models. As a result, the models with lower representational capacity but computationally more tractable than nonlinear models can be used for accurate and efficient state estimation. We evaluate the generalization performance of our methods on the 3D human pose tracking problem from monocular videos. The experiments indicate that the discriminative learning can lead to improved accuracy of pose estimation with no increase in computational cost of tracking.