Dual generative models for human motion estimation from an uncalibrated monocular camera

We propose a new approach to estimate gait kinematics from image sequences taken by a monocular uncalibrated camera. This approach involves two generative models for gait representations in the kinematic and visual spaces, which induce two gait manifolds that characterize the gait variability in terms of the kinematics and visual appearance. A manifold topology enforcement scheme is introduced to incorporate the two gait manifolds. Moreover, a new particle filtering algorithm is proposed for dynamic gait tracking and estimation where a segmental jump-diffusion Markov Chain Monte Carlo (MCMC) technique is developed to accommodate the dynamic nature of the gait variability. The proposed algorithm is trained from CMU Mocap data and tested on the HumanEva dataset with promising results.