Dual Gait Generative Models for Human Motion Estimation From a Single Camera

This paper presents a general gait representation framework for video-based human motion estimation. Specifically, we want to estimate the kinematics of an unknown gait from image sequences taken by a single camera. This approach involves two generative models, called the kinematic gait generative model (KGGM) and the visual gait generative model (VGGM), which represent the kinematics and appearances of a gait by a few latent variables, respectively. The concept of gait manifold is proposed to capture the gait variability among different individuals by which KGGM and VGGM can be integrated together, so that a new gait with unknown kinematics can be inferred from gait appearances via KGGM and VGGM. Moreover, a new particle filtering algorithm is proposed for dynamic gait estimation, which is embedded with a segmental jump-diffusion Markov Chain Monte Carlo (MCMC) scheme to accommodate the gait variability in a long observed sequence. The proposed algorithm is trained from the CMU Mocap ...

• Type: Journal
• Year: 2010
• Where: TSMC
• Authors: Xin Zhang, Guoliang Fan
• Keywords: Artificial Intelligence, Gait, Gait Generative Model, Gait Variability, TSMC 2010