Monocular human motion capture with a mixture of regressors

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 2005-September, Issue , 2005, Pages

  Agarwal, Ankur ^a   Triggs, Bill ^a  

^a INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MIXTURES; MOTION CAPTURE;

3D HUMAN MOTION CAPTURES; HUMAN MOTION CAPTURE; HUMAN SILHOUETTE; LEARNING-BASED APPROACH; MOTION CAPTURE DATA; REAL IMAGE SEQUENCES; RECONSTRUCTION PROBLEMS; TRACKING FAILURE;

COMPUTER VISION;

EID: 38149094811     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.496     Document Type: Conference Paper

References (24)

1. A. Agarwal and B. Triggs. 3D Human Pose from Silhouettes by Relevance Vector Regression. In Int. Conf. Computer Vision & Pattern Recognition, 2004.
2. A. Agarwal and B. Triggs. Learning to Track 3D Human Motion from Silhouettes. In Int. Conf. on Machine Learning, 2004.
3. V. Athitsos and S. Sclaroff. Estimating 3D Hand Pose From a Cluttered Image. In Int. Conf. Computer Vision, 2003.
4. S. Belongie, J. Malik, and J. Puzicha. Shape Matching and Object Recognition using Shape Contexts. IEEE Trans. Pattern Analysis & Machine Intelligence, 24(4):509-522, 2002.
5. M. Brand. Shadow Puppetry. In Int. Conf. Computer Vision, pages 1237-1244, 1999.
6. A. Elgammal and C. Lee. Inferring 3D Body Pose from Silhouettes using Activity Manifold Learning. In Int. Conf. Computer Vision & Pattern Recognition, 2004.
7. K. Grauman and T. Darrell. Fast Contour Matching Using Approximate Earth Mover's Distance. In Int. Conf. Computer Vision & Pattern Recognition, 2004.
8. I.Haritaoglu, D.Harwood, and L.Davis. Ghost: A Human Body Part Labeling System Using Silhouettes. In International Conference on Pattern Recognition, 1998.
9. M. Isard and A. Blake. CONDENSATION - Conditional Density Propagation for Visual Tracking. Int. J. Computer Vision, 29(1):5-28, 1998.
10. R. Jacobs, M. Jordan, S. Nowlan, and G. Hinton. Adaptive Mixtures of Local Experts. Neural Computation, 3(1):79-87, 1991.
11. G. Mori and J. Malik. Estimating Human Body Configurations Using Shape Context Matching. In European Conf. Computer Vision, volume 3, pages 666-680, 2002.
12. A. Ng, M. Jordan, and Y. Weiss. On Spectral Clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems 14, 2001.
13. V. Parameswaram and R. Chellappa. View Independent Body Pose Estimation from a Single Perspective Image. In Int. Conf. Computer Vision & Pattern Recognition, 2004.
14. R. Rosales and S. Sclaroff. Learning Body Pose via Specialized Maps. In Advances in Neural Information Processing Systems, 2001.
15. B. Schölkpof, A. Smola, and K. Müller. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 10:1299-1319, 1998.
16. G. Shakhnarovich, P. Viola, and T. Darrell. Fast Pose Estimation with Parameter Sensitive Hashing. In Int. Conf. Computer Vision, 2003.
17. H. Sidenbladh, M. Black, and L. Sigal. Implicit Probabilistic Models of Human Motion for Synthesis and Tracking. In European Conf. Computer Vision, volume 1, 2002.
18. L. Sigal, S. Bhatia, S. Roth, M. Black, and M. Isard. Tracking Loose-Limbed People. In Int. Conf. Computer Vision & Pattern Recognition, 2004.
19. C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas. Learning to Reconstruct 3D Human Motion from Bayesian Mixture of Experts. In Int. Conf. Computer Vision & Pattern Recognition, 2005.
20. C. Sminchisescu and B. Triggs. Kinematic Jump Processes For Monocular 3D Human Tracking. In Int. Conf. Computer Vision & Pattern Recognition, June 2003.
21. C. Sminchisescu and Bill Triggs. Estimating articulated human motion with covariance scaled sampling. International Journal of Robotics Research, 22(6):371-391, June 2003. Special issue on Visual Analysis of Human Movement.
22. A. Thayananthan, B. Stenger, P. Torr, and R. Cipolla. Learning a kinematic prior for tree-based filtering. In British Machine Vision Conference, volume 2, pages 589-598, 2003.
23. K. Toyama and A. Blake. Probabilistic tracking with exemplars in a metric. International Journal of Computer Vision, 48(1):9-19, 2002.
24. O. Williams, A. Blake, and R. Cipolla. A Sparse Probabilistic Learning Algorithm for Real-Time Tracking. In Int. Conf. Computer Vision, 2003.