Viewpoint invariant exemplar-based 3D human tracking

Computer Vision and Image Understanding

Volumn 104, Issue 2-3 SPEC. ISS., 2006, Pages 178-189

  Ong, Eng Jon ^a   Micilotta, Antonio S ^a   Bowden, Richard ^a   Hilton, Adrian ^a  

^a UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

3D human tracking; Exemplar based; Learnt motion model; View invariant

Indexed keywords

3D HUMAN TRACKING; EXEMPLAR BASED; LEARNT MOTION MODEL; VIEW INVARIANT;

CAMERAS; COMPUTER VISION; INFORMATION ANALYSIS; THREE DIMENSIONAL; VISUALIZATION;

IMAGE PROCESSING;

EID: 33750003126     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2006.08.004     Document Type: Article

References (24)

1. Ren L., Shakhnorovich G., Hodgins J., Pfister H., and Viola P. Learning silhoutte features for control of human motion. Proceedings of the SIGGRAPH 2004 Conference on Sketches and Applications (2004), ACM Press, New York
2. T. Heap, Learning deformable shape models for object tracking, Ph.D. thesis, School of Computer Studies, University of Leeds, UK (September 1997).
3. Bowden R., Mitchell T., and Sarhadi M. Reconstructing 3d pose and motion from a single camera view. In: Carter J.N., and Nixon M.S. (Eds). Proceedings of the British Machine Vision Conference vol. 2 (1998), University of Southampton 904-913
4. C. Sminchisescu, A. Jepson, Generative modeling for continuous non-linearly embedded visual inference, in: Proceedings of the 21st International Conference on Machine Learning, Banff, Canada, 2004.
5. A. Rahimi, B. Recht, T. Darrell, Learning appearance manifolds from video, in: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
6. A. Elgammal, C. Lee, Inferring 3d body pose from silhouettes using activity manifold learning, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, 2004, pp. 681-688.
7. G. Shakhnarovich, P. Viola, T. Darrell, Fast pose estimation with parameter-sensitive hashing, in: Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003.
8. B. Stenger, A. Thayananthan, P.H.S. Torr, R. Cipolla, Hand pose estimation using hierarchical detection, in: International Workshop on Human-Computer Interaction, Prague, Czech Republic, 2004, pp. 105-116.
9. K. Toyama, A. Blake, Probabilistic tracking in a metric space, in: Proceedings of the ICCV, 2001, pp. 50-59.
10. G. Mori, J. Malik, Estimating human body configuration using shape context matching, in: Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, 2002.
11. Jenkins O., and Matarić M. Performance-derived behaviour vocabularies: data-driven acquisition of skills from motion. International Journal of Humanoid Robotics 1 2 (2004) 237-288
12. M. Dimitrijevic, V. Lepetit, P. Fua, Human body pose recognition using spatial-temporal templates, in: ICCV Workshop on Modelling People and Human Interaction, Beijing, China, 2005.
13. C. Sminchisescu, A. Kanaujia, Z. Li, D. Metaxas, Discriminative density propagation for 3d human motion estimation, in: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
14. M. Brand, Shadow puppetry, in: Proceedings of the ICCV, 1999, pp. 1237-1244.
15. H. Sidenbladh, M.J. Black, L. Sigal, Implicit probabilistic models of human motion for synthesis and tracking, in: Proceedings of the ECCV (1), 2002, pp. 784-800.
16. N. Howe, M. Leventon, W. Freeman, Bayesian reconstructions of 3d human motion from single camera video, in: NIPS, 1999.
17. R. Urtasun, P. Fua, 3d human body tracking using deterministic motion models, in: Proceedings of the ECCV, 2004.
18. Agarwal A., and Triggs B. Recovering 3d human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 1 (2006) 44-58
19. D. Ramanan, D. Forsyth, Finding and tracking people from the bottom up, in: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2003.
20. L. Sigal, M. Isard, B. Sigelman, M. Black, Attractive people: assembling loose-limbed models using non-parametric belief propagation, in: Advances in Neural Information Processing, 2003, pp. 1539-1546.
21. Isard M., and Blake A. Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision 29 1 (1998) 5-28
22. P. Felzenszwalb, D. Hurrenlocher, Distance transforms of sampled functions, Technical Report TR2004-1963, Cornell Computing and Information Science (2004).
23. H. Barrow, J. Tenenbaum, R. Bolles, H. Wolf, Parametric correspondence and chamfer matching: two new techniques for image matching, in: Proceedings of Joint Conference on Artificial Intelligence, 1977, pp. 659-663.
24. Arikan O., and Forsyth D. Interactive motion generation from examples. ACM Transactions on Graphics 21 (2002) 483-490