Outdoor Human Motion Capture using Inverse Kinematics and von Mises-Fisher Sampling

Human motion capturing (HMC) from multiview image sequences constitutes an extremely difficult problem due to depth and orientation ambiguities and the high dimensionality of the state space. In this paper, we introduce a novel hybrid HMC system that combines video input with sparse inertial sensor input. Employing an annealing particle-based optimization scheme, our idea is to use orientation cues derived from the inertial input to sample particles from the manifold of valid poses. Then, visual cues derived from the video input are used to weight these particles and to iteratively derive the final pose. As our main contribution, we propose an efficient sampling procedure where hypothesis are derived analytically using state decomposition and inverse kinematics on the orientation cues. Additionally, we introduce a novel sensor noise model to account for uncertainties based on the von Mises-Fisher distribution. Doing so, orientation constraints are naturally fulfilled and the number of ...

• Type: Conference
• Year: 2011
• Where: ICCV
• Authors: Gerard Pons-Moll, Andreas Baak, Juergen Gall, Laura Leal-Taixe, Meinard Mueller, Hans-Peter Seidel, Bodo Rosenhahn
• Keywords: Human Pose Estimation, Inverse Kinematics, Manifold, Tracking