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

MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops

Volumn , Issue , 2008, Pages 69-76

  Kim, Daehwan ^a   Kim, Daijin ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Human body motion tracking; ICP; Particle filter

Indexed keywords

3D HUMAN BODY; CONVERGENCE RATES; FITTING ALGORITHMS; HUMAN BODY MOTION; HUMAN BODY MOTION TRACKING; ICP ALGORITHMS; ITERATIVE CLOSEST POINT; MODEL-BASED; MOTION HISTORY; MOTION PARAMETERS; OBSERVATION MODEL; PARTICLE FILTER; REGISTRATION ALGORITHMS; SURFACE NORMALS;

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; DISTRIBUTED COMPUTER SYSTEMS; INDUCTIVELY COUPLED PLASMA; MOTION ESTIMATION; NONLINEAR FILTERING; TARGET TRACKING; TECHNICAL PRESENTATIONS; THREE DIMENSIONAL;

THREE DIMENSIONAL COMPUTER GRAPHICS;

EID: 77950463372     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1461893.1461907     Document Type: Conference Paper

References (14)

1. P. Besl and N. MacKay. A method for registration of 3-d shapes. IEEE Transantions on Pattern Analysis and Machine Intelligence, 14(2):239-256, 1992.
2. C. Bregler and J. Malik. Tracking people with twists and exponential maps. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pages 8-15, 1998.
3. Y. Chen and G. Medioni. Object modeling by registration of multiple range images. Image and Vision Computing, 10(3):145-155, 1991.
4. Q. Delamarre and O. Faugeras. 3d articulated models and multi-view tracking with silhouettes. In Proc. of the International Conference on Computer Vision, pages 716-721, 1999.
5. D. Demirdjian. Enforcing constraints for human body tracking. In Workshop on Multiple Object Tracking, pages 102-1093, 2003.
6. J. Deutscher, A. Davision, and I. Reid. Articulated body motion capture by annealed particle filtering. In Proc. of Computer Vision and Pattern Recognition, pages 126-133, 2000.
7. A. Doucet, J. Godsill, and C. Andrieu. On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10(3):197-209, 2000.
8. W. Liang, H.Weiming, and T. Tieniu. Recent developments in human motion analysis. Pattern Recognition, 36(3):585-601, 2003.
9. T. Moeslund, A. Hilton, and V. Kruger. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding, 104(2-3):90-126, 2006.
10. O. Munkelt, C. Ridder, D. Hansel, and W. Hafner. A model driven 3D image interpretation system applied to person detection in video images. In Proc. of the International Conference on Pattern Recognition, pages 70-73, 1998.
11. K. Okuma, A. Taleghani, N. Freitas, J. Little, and D. Lowe. A boosted particle filter: multitarget detection and tracking. In Proc. of European Conference on Computer Vision, pages 28-39, 2004.
12. K. Rohr. Towards model-based recognition of human movements in image sequeces. CVGIP: Image Understanding, 59(1):94-115, 1994.
13. S. Rusinkiewicz and M. Levoy. Efficient variants of the ICP algorithm. In Proc. of International Conference on 3D Digital Imaging and Modeling, pages 145-152, 2001.
14. H. Sidenbladh, M. Black, and D. Fleet. Stochastic tracking of 3D human figures using 2D image motion. In Proc. of International Conference on Computer Vision, pages 702-718, 2000.