Sparse probabilistic regression for activity-independent human pose inference

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Urtasun, Raquel ^a   Darrell, Trevor ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ERRORS; COVARIANCE FUNCTIONS; HIGH-DIMENSIONAL; HUMAN POSE; HYPERPARAMETERS; LOCAL REGRESSIONS; MIXTURE COMPONENTS; MIXTURE OF GAUSSIAN; MONOTONICALLY; MULTI-MODAL; POSE SPACE; PROBABILISTIC APPROACHES; PROBABILISTIC REGRESSION; TRAINING SETS; VISUAL OBSERVATIONS;

ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; COMPUTER VISION; CONFORMAL MAPPING; FEATURE EXTRACTION; FOOD PROCESSING; IMAGE PROCESSING; KETONES; MOTION ESTIMATION; PATTERN RECOGNITION; PROBABILITY; REGRESSION ANALYSIS;

DATABASE SYSTEMS;

EID: 51949116853     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587360     Document Type: Conference Paper

References (23)

1. A. Agarwal and B. Triggs. Recovering 3D human pose from monocular images. PAMI 28(1):44-58, January 2006.
2. V. Athitsos and S. Sclaroff. Database indexing methods for 3d hand pose estimation. In Gesture Workshop, 2003.
3. C. G. Atkeson, A. W. Moore and S. Schaal. Locally weighted learning. Artif. Intell. Rev., 11:11-73, 1997.
4. P. F. Felzenszwalb and D. P.Huttenlocher Efficient matching of pictorial structures . CVPR 2000.
5. K. Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. ICCV Beijing, China, October 2005.
6. Humaneva. http://vision.cs.brown.edu/humaneva/.
7. A. Kanaujia, C. Sminchisescu, and D. Metaxas. Semi-supervised Hierarchical Models for 3D Human Pose Reconstruction. CVPR 2007.
8. N. D. Lawrence, M. Seeger, and R. Herbrich. Fast sparse Gaussian process methods: The informative vector machine. NIPS pages 609-616. 2003.
9. C.S. Lee and A. Elgammal. Body Pose Tracking From Uncalibrated Camera Using Supervised Manifold Learning. In NIPS EHUM Workshop, 2006.
10. T. Liu, A. Moore, A. Gray. Efficient exact k-nn and nonparametric classification in high dimensions. NIPS 2003.
11. Mocap data. http://www.mocapdata.com.
12. R. Navaratnam, A. Fitzgibbon and R. Cipolla. The Joint Manifold Model for Semi-supervised Multi-valued Regression. ICCV Rio de Janeiro, Brazil, October 2007.
13. K. Moon and V. Pavlovic. Impact of Dynamics on Subspace Embedding and Tracking of Sequences. CVPR 2006.
14. J. Qui.nonero-Candela and C. E. Rasmussen. A unifying view of sparse approximate gaussian process regression. Journal of Machine Learning Research, 2005.
15. D. Ramanan, D.A.Forsyth and A. Zisserman. Tracking people by learning their appearance. PAMI 2007.
16. C. E. Rasmussen and C. K. Williams. Gaussian Process for Machine Learning. MIT Press, 2006.
17. R. Rosales and S. Sclaroff. Infering Body Pose without Tracking Body Parts. CVPR pages 506-511, 2000.
18. G. Shakhnarovich, P. Viola, T. Darrell. Fast pose estimation with parameter-sensitive hashing. ICCV 2003.
19. C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas. Discriminative Density Propagation for 3D Human Motion Estimation. CVPR 2005.
20. E. Snelson and Z. Ghahramani. Sparse gaussian processes using pseudo-inputs. NIPS, 2006.
21. T. Tian, R. Li, and S. Sclaroff. Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions. In CVPR Learning Workshop, volume 3, San Diego, CA, 2005.
22. R. Urtasun, D. J. Fleet, and P. Fua. 3D people tracking with gaussian process dynamical models. CVPR 2006.
23. R. Urtasun, D. J. Fleet, A. Hertzman, and P. Fua. Priors for people tracking from small training sets. ICCV pages 403-410, Beijing, China, October 2005.