Reconstruction of 3D human body pose from stereo image sequences based on top-down learning

Pattern Recognition

Volumn 40, Issue 11, 2007, Pages 3120-3131

  Yang, Hee Deok ^a   Lee, Seong Whan ^a  

^a Korea University   (South Korea)

Author keywords

3D human modeling; Depth information; Reconstruction of 3D human body pose; Spatio temporal features

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; LEARNING SYSTEMS; MATHEMATICAL MODELS; STATISTICAL METHODS; THREE DIMENSIONAL COMPUTER GRAPHICS;

3D HUMAN MODELING; DEPTH INFORMATION; RECONSTRUCTION OF 3D HUMAN BODY POSE; SPATIO TEMPORAL FEATURES;

IMAGE RECONSTRUCTION;

EID: 34250844335     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.01.033     Document Type: Article

References (25)

1. R. Rosales, S. Sclaroff, Specialized mapping and the estimation of human body pose from a single image, Proceedings of IEEE Workshop on Human Motion, TX, USA, December 2000, pp. 19-24.
2. Ullman S., and Basri R. Recognition by linear combinations of models. IEEE Trans. Pattern Anal. Mach. Intell. 13 10 (1991) 992-1006
3. A. Agarwal, B. Triggs, 3D human pose from silhouette by relevance vector regression, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC, USA, July 2004, pp. 882-888.
4. Bowden R., Mitchell T.A., and Sarhadi M. Non-linear statistical models for 3D reconstruction of human pose and motion from monocular image sequences. Image Vision Comput. 18 9 (2000) 729-737
5. M. Brand, Shadow puppetry, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kerkyra, Greece, 1999, pp. 1237-2344.
6. D.M. Gavrilla, L.S. Davis, Towards 3-D model-based tracking and recognition of human movement: a multi-view approach, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 1996, pp. 73-80.
7. T. Heap, D. Hogg, Improving specificity in PDMs using a hierarchical approach, Proceedings of the Eighth British Machine Vision Conference, Colchester, UK, September 1997, pp. 590-599.
8. Howe N., Leventon M., and Freeman M. Bayesian reconstruction 3D human motion from single-camera video. Adv. Neural Inf. Process. Syst. 12 (2000) 820-826
9. G. Mori, et al., Recovering human body configurations: combining segmentation and recognition, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC, USA, 2004, pp. 326-333.
10. E.J. Ong, S. Gong, A dynamic human model using hybrid 2D-3D representations in hierarchical PCA space, Proceedings of 10th British Machine Vision Conference, Nottingham, UK, September 1999, pp. 33-42.
11. G. Shakhnarovich, P. Viola, T. Darrell, Fast pose estimation with parameter sensitive hashing, Proceedings of Ninth International Conference on Computer Vision, Nice, France, October 2003, pp. 750-757.
12. Sminchisescu C., and Triggs B. Estimating articulated human motion with covariance scaled sampling. Int. J. Robotics Res. 22 6 (2003) 371-393
13. A.H. Barr, Global and local deformations of solid primitives, SIGGRAPH, 1984, pp. 21-30.
14. Y. Song, X. Feng, P. Perona, Towards detection of human motion, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, SC, USA, June 2000, pp. 810-817.
15. Tolani D., Goswami A., and Badler N. Real-time inverse kinematics techniques for anthropomorphic limbs. Graphical Models 62 5 (2000) 353-388
16. Bobick A., and Davis J. The representation and recognition of action using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23 3 (2001) 257-267
17. B.-W. Hwang, S. Kim, S.-W. Lee, Full-body gesture database for analyzing daily human gestures, Proceedings of the First International Conference on Intelligent Computing, Hefei, China, August 2005, pp. 611-620, The KU Gesture Database, 〈http://GestureDB.korea.ac.kr/〉.
18. Y. Chen, J. Lee, R. Parent, R. Machiraju, Markerless monocular motion capture using image features and physical constraints, Computer Graphics International 2005, Stony Brook, USA, June 2005, pp. 36-43.
19. Infantino I., Chella A., Dindo H., and Macaluso I. A cognitive architecture for robotic hand posture learning. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 35 1 (2005) 42-52
20. M. Jones, T. Poggio, Model-based matching of line drawings by linear combination of prototypes, Proceedings of the IEEE Fifth International Conference on Computer Vision, Cambridge, UK, 1995, pp. 531-536.
21. S.U. Lee, I. Cohen, 3D hand reconstruction from a monocular view, Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 310-313.
22. Solina F., and Bajcsy R. Recovery of parametric models from range images: the case for superquadrics with global deformation. IEEE Trans. Pattern Anal. Mach. Intell. 12 2 (1990) 131-147
23. Taylor C. Reconstruction of articulated objects from point correspondences in a single uncalibrated image. Comput. Vision Image Understanding 80 3 (2000) 349-363
24. Terzopoulos D., and Metaxas D. Dynamic 3D models with local and global deformations: deformable superquadrics. IEEE Trans. Pattern Anal. Mach. Intell. 13 7 (1991) 703-714
25. Wu Y., and Huang T.S. Hand modeling analysis and recognition. IEEE Signal Processing Magazine 18 3 (2001) 51-60