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

作者：Zhang Xin^*; Fan Guoliang

来源：IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics , 2010, 40(4): 1034-1049.

DOI：10.1109/TSMCB.2010.2044240

• Gait appearances
• gait kinematics
• generative models
• human motion estimation
• manifold learning
• Markov chain Monte Carlo (MCMC)
• particle filtering
• tensor analysis

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 scheme to accommodate the gait variability in a long observed sequence. The proposed algorithm is trained from the Carnegie Mellon University (CMU) Mocap data and tested on the Brown University HumanEva data with promising results.

• 出版日期2010-8