Multiple hypothesis tracking for automatic optical motion capture

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2350, Issue , 2002, Pages 524-536

  Ringer, Maurice ^a   Lasenby, Joan ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Correspondence problem; Optical motion capture; Tracking; Visual motion

Indexed keywords

BAYESIAN NETWORKS; COMPUTER VISION; MOTION CAPTURE; SURFACE DISCHARGES;

BAYESIAN APPROACHES; CORRESPONDENCE PROBLEMS; HUMAN OPERATOR; MULTIPLE HYPOTHESIS TRACKING; OPTICAL MOTION CAPTURE; TIME FRAME; TRACKING TECHNIQUES; VISUAL MOTION;

MOTION TRACKING;

EID: 46149122030     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47969-4_35     Document Type: Conference Paper

References (19)

1. S. Blackman and R. Popoli. Design and analysis of modern tracking systems. Artech House, 1999.
2. G. Carpaneto and P. Toth. Algorithm 548: Solution of the assignment problem [H]. ACM Transactions on Mathematical Software, 6(1):104-111, March 1980.
3. T.-J. Cham and J. Rehg. A multiple hypothesis approach to figure tracking. In Proc. 1999 Comp Vision and Pattern Recognition (CVPR 99), volume 2, pages 239-245, Fort Collins, USA, 1999.
4. I. J. Cox and S. L. Hingorani. An efficient implementation and evaluation of Reid’s multiple hypothesis tracking algorithm for visual tracking. IEEE Trans. on PAMI, 18(2):138-150, February 1996.
5. R. Danchick and G. Newnam. A fast method for finding the exact N-best hypothesis for multitarget tracking. In IEEE trans on Aerospace and Elec Sys, volume 29, pages 555-560, Apr 1993.
6. D. G. Forney Jr. The Viterbi algorithm. Proceedings of the IEEE, 61(3):268-268, March 1973.
7. S. J. Godsill, A. Doucet, and M. West. Maximum a posteriori sequence estimation using Monte Carlo particle filters. Ann. Inst. Statist. Math, 52(1), March 2001.
8. N. Gordon, D. Salmond, and A. Smith. Novel approach to nonlinear/non-gaussian bayesian state estimation. In IEEE Proc F, No 140, pages 107-113, 1993.
9. L. Herda, P. Fua, R. Plänkers, R. Boulic, and D. Thalmann. Skelton-based motion capture for robust reconstruction of human motion. In Proc. Computer Animation, IEEE CS Press, 2000.
10. A. Menache. Understanding Motion Capture for Computer Animation and Video Games. Korgan Kaufmann Publishers, Academic Press, 2000.
11. C. Rasmussen and G. D. Hager. Joint porbabilistic techniques for tracking multipart objects. In Proc. Computer Vision and Pattern Recognition (CVPR), pages 16-21, 1998.
12. J. Rehg and T. Kanade. Visual tracking of self-occluding articulated objects. In Proc of the International Conf on Computer Vision (ICCV), Boston, USA, June 1995.
13. D. Reid. An algorithm for tracking multiple targets. In IEEE Trans on Automatic Control, volume AC-24, pages 843-854, Dec 1979.
14. M. Ringer, T. Drummond, and J. Lasenby. Using occlusions to aid pose estimation for visual motion capture. In Proc. Computer Vision and Pattern Recognition (CVPR), Kauai, USA, 2001.
15. M. Ringer and J. Lasenby. Modelling and tracking articulated motion from multiple camera views. In Proc. 11th British Machine Vision Conference (BMVC2000), volume 1, pages 172-181, Bristol, UK, September 2000.
16. L. Schiff. The future of motion-capture animation: Building the perfect digital human. Animation World Magazine, Issue 4.11, February 2000.
17. Y. Song, L. Goncalves, E. Di Bernardo, and P. Perona. Monocular perception of biological motion-detection and labeling. In Proc. 7th Int. Conf. on Computer Vision (ICCV99), pages 805-812, Corfu, Greece, 1999.
18. Y. Song, L. Goncalves, and P. Perona. Monocular perception of biological motion-clutter and partial occlusion. In Proc. 6th European Conf. on Computer Vision (ECCV00), volume 2, pages 719-733, Dublin, Ireland, 2000.
19. H. A. Taha. Operations Research, An Introduction. Prentice-Hall, 6th edition, 1997