A novel fitting algorithm using the ICP and the particle filters for robust 3d human body motion tracking

This paper proposes a novel fitting algorithm using the iterative closest point (ICP) registration algorithm and the particle filters for robust 3D human body motion tracking. We use the ICP registration algorithm that fits the 3D human body model to the 3D articulation data in a hierarchical manner. However, it often can not fit under the rapidly moving human body motion. To solve this problem, we combine the modified particle filter with the ICP algorithm. It can search the most appropriate motion parameters by using the observation model based on the surface normal vector and the binary valued function and the state transitional model based on the motion history information. Experimental results show that the proposed combined fitting algorithm provides accurate fitting performance and high convergence rate.

[1]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[2]  Jitendra Malik,et al.  Tracking people with twists and exponential maps , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[3]  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).

[4]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[6]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[7]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[8]  Olivier D. Faugeras,et al.  3D articulated models and multi-view tracking with silhouettes , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  K. Rohr.  Towards model-based recognition of human movements in image sequences , 1994 .

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

[11]  David Hansel,et al.  A model driven 3D image interpretation system applied to person detection in video images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[12]  David Demirdjian.  Enforcing Constraints for Human Body Tracking , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

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

[14]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[15]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..