The Kneed Walker for human pose tracking

The Kneed Walker is a physics-based model derived from a planar biomechanical characterization of human locomotion. By controlling torques at the knees, hips and torso, the model captures a full range of walking motions with foot contact and balance. Constraints are used to properly handle ground collisions and joint limits. A prior density over walking motions is based on dynamics that are optimized for efficient cyclic gaits over a wide range of natural human walking speeds and step lengths, on different slopes. The generative model used for monocular tracking comprises the Kneed Walker prior, a 3D kinematic model constrained to be consistent with the underlying dynamics, and a simple measurement model in terms of appearance and optical flow. The tracker is applied to people walking with varying speeds, on hills, and with occlusion.

[1]  Russ Tedrake,et al.  Efficient Bipedal Robots Based on Passive-Dynamic Walkers , 2005, Science.

[2]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  D. Gordon E. Robertson,et al.  Research Methods in Biomechanics , 2004 .

[4]  Ankur Agarwal,et al.  Recovering 3D human pose from monocular images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  L. Shampine Conservation laws and the numerical solution of ODEs, II , 1999 .

[6]  Rui Li,et al.  Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Tad McGeer,et al.  Passive walking with knees , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[8]  David J. Fleet,et al.  3D People Tracking with Gaussian Process Dynamical Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  A. Kuo A simple model of bipedal walking predicts the preferred speed-step length relationship. , 2001, Journal of biomechanical engineering.

[10]  Cristian Sminchisescu,et al.  BM³E : Discriminative Density Propagation for Visual Tracking , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Alessandro Bissacco,et al.  Modeling and learning contact dynamics in human motion , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  B. Brogliato,et al.  Numerical simulation of finite dimensional multibody nonsmooth mechanical systems , 2001 .

[13]  David J. Fleet,et al.  Optical Flow Estimation , 2006, Handbook of Mathematical Models in Computer Vision.

[14]  Arthur D Kuo,et al.  Energetics of actively powered locomotion using the simplest walking model. , 2002, Journal of biomechanical engineering.

[15]  Andrew P. Witkin,et al.  Spacetime constraints , 1988, SIGGRAPH.

[16]  A. Schwab Lecture Notes Multibody Dynamics B, wb1413 , 2009 .

[17]  Alex Pentland,et al.  Dynamic models of human motion , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[18]  Cristian Sminchisescu,et al.  Generative modeling for continuous non-linearly embedded visual inference , 2004, ICML.

[19]  David J. Fleet,et al.  Stochastic Tracking of 3 D Human Figures Using 2 D Image Motion , 2000 .

[20]  Stephen J. Wright,et al.  Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .

[21]  Lawrence F. Shampine,et al.  Non-negative solutions of ODEs , 2005, Appl. Math. Comput..

[22]  C. Karen Liu,et al.  Learning physics-based motion style with nonlinear inverse optimization , 2005, ACM Trans. Graph..

[23]  A. Elgammal,et al.  Inferring 3D body pose from silhouettes using activity manifold learning , 2004, CVPR 2004.

[24]  Cristian Sminchisescu,et al.  Kinematic jump processes for monocular 3D human tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[25]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[26]  Jessica K. Hodgins,et al.  Animation of dynamic legged locomotion , 1991, SIGGRAPH.

[27]  Björn Stenger,et al.  Multivariate Relevance Vector Machines for Tracking , 2006, ECCV.

[28]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[29]  T McGeer,et al.  Dynamics and control of bipedal locomotion. , 1993, Journal of theoretical biology.

[30]  C. K. Liu,et al.  Learning physics-based motion style with nonlinear inverse optimization , 2005, SIGGRAPH 2005.

[31]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[32]  K HodginsJessica,et al.  Animation of dynamic legged locomotion , 1991 .

[33]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

[34]  Marc H. Raibert,et al.  Legged Robots That Balance , 1986, IEEE Expert.

[35]  David J. Fleet,et al.  Physics-Based Person Tracking Using Simplified Lower-Body Dynamics , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.