Modeling Human Gaits with Subtleties

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 36, Issue 16, 2003, Pages 1375-1380

  Bissacco, Alessandro ^a   Saisan, Payam ^b   Soatto, Stefano ^a  

^a Department of Civil and Environmental Engineering   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

animation; dynamic ICA; higher order statistics; human motion modeling; Image based modeling and rendering

Indexed keywords

ANIMATION; DYNAMICAL SYSTEMS; IMAGE SEGMENTATION; STOCHASTIC SYSTEMS;

HUMAN MOTION MODELING; IMAGE-BASED MODELING AND RENDERING; INDEPENDENT COMPONENTS; NON GAUSSIAN INPUTS; OPTIMALITY CRITERIA; SIGNAL DISTRIBUTION; STATISTICAL INFORMATION; STOCHASTIC INPUTS;

HIGHER ORDER STATISTICS;

EID: 33748642228     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/S1474-6670(17)34952-2     Document Type: Conference Paper

References (24)

1. A. Bissacco, A. Chiuso, Y. Ma, and S. Soatto Recognition of human gaits Proc. of the IEEE Intl. Conf. on Comp. Vision and Patt Recog 2001 401 417
2. S. Amari Natural gradient works efficiently in learning Neural Computation 10 1998 251 276
3. M.J. Black Eigentracking: robust matching and tracking of articulated objects 1996
4. M.J. Black Explaining optical flow events with parameterized spatio-temporal models Proc. of Conference on Computer Vision and Pattern Recognition 1 1999 326 332
5. M.J. Black, and A.D. Jepson A probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions Proc. of European Conference on Computer Vision 1 1998 909 924
6. A.F. Bobick Appearance-based representation of action 1996
7. M. Brand Morphable 3d models from video Proc. International Conference on Computer Vision and Pattern Recognition 2001
8. C. Bregler Learning and recognizing human dynamics in video sequences Proc. of the Conference on Computer Vision and Pattern Recognition 1997 568 574
9. C. Bregler Tracking people with twists and exponential maps Proc. International Conference on Computer Vision and Pattern Recognition 1998
10. L. Campbell, and A. Bobick Recognition of humau body motion using phase space constraints Proc. IEEE Conf. on Comp. Vision and Pattern Recogn 1995 8
11. P. Comon Independent component analysis, a new concept Signal Processing 36 1994 287 314
12. D.M. Gavrila, and L.S. Davis Tracking of humans in action: a 3-d model-based approach 1996
13. S. Geman, and D. Geman Stochastic relaxation, gibbs distributions. and the Bayesian restoration of images IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI -6 1984 721 741
14. M.A. Giese, and T. Poggio Morphable models for the analysis and synthesis of complex motion patterns International Journal of Computer Vision 38 1 2000 1264 1274
15. G. Johansson Visual perception of biological motion and a model for its analysis Perception and Psychophysics 14 2 1973 201 211
16. J.J. Little, and J.E. Boyd Recognizing people by their gait: the shape of motion 1996
17. A.A. Niyogi Analyzing and recognizing walking figures in xyt In Proc. IEEE Conf. on Comp. Vision and Pattern Recogn Seattle 1994 469 474 June
18. B. North, A. Blake, M. Isard, and J. Rittscher Learning and classification of complex dynamics IEEE Transaction on Pattern Analysis and Machine Intelligence 22 9 2000 1016 1034
19. P.Van Overschee, and B. De Moor Subspace algorithms for the stochastic identification problem Automatica 29 1993 649 660
20. V. Pavlovic, J. Rehg, and J. MacCormick Impact of dynamic model learning on classification of human motio Proc. International Conference on Computer Vision and Pattern Recognition 2000
21. L. Torresani, D. Yang, G. Alexander, and C. Bregler Tracking and modelling non-rigid objects with rank constraint Proc. International Conference on Computer Vision and Pattern Recognition 2001
22. C. Wren Dynamic models of human motion 1998
23. Y. Yacoob, and M.J. Black Parameterized modeling and recognition of activities Computer Vision and Image Understanding 73 2 1999 232 247
24. L. Zhang, and A. Cichock Blind deconvolution of dynamical systems: A state-space approach Proceedings of the IEEE. Workshop on NNSP'98 1998 123 131