Learning to track 3D human motion from silhouettes

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 9-16

  Agarwal, Ankur ^a   Triggs, Bill ^a  

^a GRAVIR INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ENTROPY; HEURISTIC METHODS; IMAGE SEGMENTATION; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; MOTION ESTIMATION; REGRESSION ANALYSIS; VECTORS; WEBSITES;

CLUSTERING; MONOCULAR SILHOUETTES; PARTICLE FILTERS; SILHOUETTE EXTRACTION;

LEARNING SYSTEMS;

EID: 14344254997     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Agarwal, A., & Triggs, B. (2004a). 3D Human Pose from Silhouettes by Relevance Vector Regression. Int. Conf. Computer Vision & Pattern Recognition.
2. Agarwal, A., & Triggs, B. (2004b). Tracking Articulated Motion with Piecewise Learned Dynamical Models. European Conf. Computer Vision.
3. Athitsos, V., & Sclaroff, S. (2000). Inferring Body Pose without Tracking Body Parts. Int. Conf. Computer Vision & Pattern Recognition.
4. Athitsos, V., & Sclaroff, S. (2003). Estimating 3D Hand Pose From a Cluttered Image. Int. Conf. Computer Vision.
5. Belongie, S., Malik, J., & Puzicha, J. (2002). Shape Matching and Object Recognition using Shape Contexts. IEEE Trans. Pattern Analysis & Machine Intelligence, 24, 509-522.
6. Bishop, C. (1995). Neural Networks for Pattern Recognition, chapter 6. Oxford University Press.
7. Brand, M. (1999). Shadow Puppetry. Int. Conf. Computer Vision (pp. 1237-1244).
8. Bregler, C., & Malik, J. (1998). Tracking People with Twists and Exponential Maps. Int. Conf. Computer Vision & Pattern Recognition (pp. 8-15).
9. D'Souza, A., Vijayakumar, S., & Schaal, S. (2001). Learning Inverse Kinematics. Int. Conf. on Intelligent Robots and Systems.
10. Grauman, K., Shakhnarovich, G., & Darrell, T. (2003). Inferring 3D Structure with a Statistical Image-Based Shape Model. Int. Conf. Computer Vision (pp. 641-648).
11. Howe, N., Leventon, M., & Freeman, W. (1999). Bayesian Reconstruction of 3D Human Motion from Single-Camera Video. Neural Information Processing Systems.
12. June, F., & Dhome, M. (2002). Hyperplane Approximation for Template Matching. IEEE Trans. Pattern Analysis & Machine Intelligence, 24, 996-1000.
13. Lowe, D. (1999). Object Recognition from Local Scale-invariant
14. Features. Int. Conf. Computer Vision (pp. 1150-1157).
15. Mon, G., & Malik, J. (2002). Estimating Human Body Configurations Using Shape Context Matching. European Conf. Computer Vision (pp. 666-680).
16. Ormoneit, D., Sidenbladh, H., Black, M., & Hastie, T. (2000). Learning and Tracking Cyclic Human Motion. Neural Information Processing Systems (pp. 894-900).
17. Pavlovic, V., Rehg, J., & MacCormick, J. (2000). Learning Switching Linear Models of Human Motion. Neural Information Processing Systems (pp. 981-987).
18. Rubner, Y., Tomasi, C., & Guibas, L. (1998). A Metric for Distributions with Applications to Image Databases. Int. Conf. Computer Vision. Bombay.
19. Shakhnarovich, G., Viola, P., & Darrell, T. (2003). Fast Pose Estimation with Parameter Sensitive Hashing. Int. Conf. Computer Vision.
20. Sidenbladh, H., Black, M., & Sigal, L. (2002). Implicit Probabilistic Models of Human Motion for Synthesis and Tracking. European Conf. Computer Vision.
21. Sminchisescu, C., & Triggs, B. (2003). Kinematic Jump Processes For Monocular 3D Human Tracking. Int. Conf. Computer Vision & Pattern Recognition.
22. Stenger, B., Thayananthan, A., Torr, P., & Cipolla, R. (2003). Filtering Using a Tree-Based Estimator. Int. Conf. Computer Vision.
23. Taylor, C. (2000). Reconstruction of Articulated Objects from Point Correspondances in a Single Uncalibrated Image. Int. Conf. Computer Vision & Pattern Recognition.
24. Tipping, M. (2000). The Relevance Vector Machine. Neural Information Processing Systems.
25. Tipping, M. (2001). Sparse Bayesian Learning and the Relevance Vector Machine. J. Machine Learning Research, 1, 211-244.
26. Williams, O., Blake, A., & Cipolla, R. (2003). A Sparse Probabilistic Learning Algorithm for Real-Time Tracking. Int. Conf. Computer Vision.