Hybrid Monte Carlo filtering: edge-based people tracking

Statistical inefficiency often limits the effectiveness of particle filters for high-dimensional Bayesian tracking problems. To improve sampling efficiency on continuous domains, we propose the use of a particle filter with hybrid Monte Carlo (HMC), an MCMC (Markov chain Monte Carlo) method that follows posterior gradients toward. high probability states, while ensuring a properly weighted approximation to the posterior. We use HMC filtering to infer the 3D shape and motion of people from natural, monocular image sequences. The approach currently uses an empirical, edge-based likelihood function, and a second-order dynamic model with soft biomechanical joint constraints.

[1]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[2]  A. Kennedy,et al.  Hybrid Monte Carlo , 1987 .

[3]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  David J. Fleet,et al.  Stability of phase information , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[5]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[6]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[7]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[8]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[9]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[10]  P. Fearnhead,et al.  Improved particle filter for nonlinear problems , 1999 .

[11]  James M. Rehg,et al.  A multiple hypothesis approach to figure tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[12]  David J. Fleet,et al.  Probabilistic detection and tracking of motion discontinuities , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[14]  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).

[15]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.

[16]  Michael A. West,et al.  Combined Parameter and State Estimation in Simulation-Based Filtering , 2001, Sequential Monte Carlo Methods in Practice.

[17]  David J. Fleet,et al.  People tracking using hybrid Monte Carlo filtering , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[18]  R. Plankers,et al.  Articulated soft objects for video-based body modeling , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[19]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[20]  David J. Fleet,et al.  Probabilistic Detection and Tracking of Motion Boundaries , 2000, International Journal of Computer Vision.

[21]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.