Conditional models for contextual human motion Recognition

Proceedings of the IEEE International Conference on Computer Vision

Volumn II, Issue , 2005, Pages 1808-1815

  Sminchbescu, Cristian ^a,b,c   Kanaujia, Atul ^c   Li, Zhiguo ^c   Metaxas, Dimitris ^c  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

^c RUTGERS UNIVERSITY   (United States)

Author keywords

Conditional models; Discriminative models; Feature selection; Hidden Markov Models; Human motion recognition; Markov random fields; Multlclass logistic regression; Optimization

Indexed keywords

ADAPTIVE ALGORITHMS; DYNAMIC PROGRAMMING; GRAPH THEORY; MARKOV PROCESSES; MATHEMATICAL MODELS; MOTION ESTIMATION; OPTIMIZATION;

CONDITIONAL MODELS; DISCRIMINATIVE MODELS; FEATURE SELECTION; HIDDEN MARKOV MODELS (HMM); HUMAN MOTION RECOGNITION; MARKOV RANDOM FIELDS; MULTLCLASS LOGISTIC REGRESSION;

PATTERN RECOGNITION;

EID: 33745893539     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2005.59     Document Type: Conference Paper

References (29)

1. CMU Human Motion Capture DataBase. Available online at http://mocap.cs.cmu.edu/search.html, 2003.
2. J. Aggarwal and Q. Cai. Human Motion Analysis: A Review. CVIU, 73(3):428-40, 1999.
3. M. Black and A. Jepson. A probabilistic framework for matching temporal trajectories:Condensation-based recognition of gestures and expressions. In ECCV, 1998.
4. A. Blake, B. North, and M. Isard. Learning Multi-Class Dynamics. NIPS, 11:389-395, 1999.
5. A. Bobick and J. Davis. The recognition of human movement using temporal templates. In PAMI, 2001.
6. M. Brand, N. Oliver, and A. Pentland. Coupled Hidded Markov models for complex action recognition. In CVPR, 1996.
7. C. Bregler. Learning and recognizing human dynamics in video sequences. In CVPR, 1997.
8. R. Fablet and P. Bouthemy. Non-parametric motion recognition using temporal multiscale Gibbs models. In CVPR, 2001.
9. D. Gavrila. The Visual Analysis of Human Movement: A Survey. CVIU, 73(l):82-98, 1999.
10. S. Gong and T. Xing. Recognition of group activities using dynamic probabilistic networks. In ICCV, 2003.
11. X. He, R. Zemel, and M. Carreira-Perpinan. Multiscale conditional random fields for image labeling. In CVPR, 2004.
12. M. Jordan. Learning in graphical models. MIT Press, 2001.
13. S. Kumar and M. Hebert. Discriminative random fields: A discriminative framework for contextual interaction and classification. In ICCV, 2003.
14. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
15. A. McCallum. Efficiently inducing features of conditional random fields. In UAI, 2003.
16. A. McCallum, D. Freitag, and F. Pereira. Maximum entropy Markov models for information extraction and segmentation. In ICML, 2000.
17. V. Pavlovic and J. Rehg. Imact of dynamic model learning on the classification of human motion. In CVPR, 2000.
18. A. Quattoni, M. Collins, and T. Darrell. Conditional random fileds for object recognition. In NIPS, 2004.
19. N. Quian and T. Sejnowsky. Predicting the secondary structure of globular proteins using neural network models. J. Mol. Bio., 1988.
20. L. Rabiner. A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. IEEE, 77(2):257-286, 1989.
21. D. Ramanan and D. Forsyth. Automatic annotation of everyday movements. In NIPS, 2003.
22. Y. Shi, Y. Huang, D. Minnen, A. Bobick, and I. Essa. Propagation networks for recognition of partially ordered sequential action. In CVPR, 2004.
23. C. Sminchisescu and A. Jepson. Variational Mixture Smoothing for Non-Linear Dynamical Systems. In CVPR, volume 2, pages 608-615, Washington D.C., 2004.
24. C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas. Conditional models for human motion recognition. Technical Report CSRG-517, University of Toronto, March 2005.
25. C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas. Discriminative Density Propagation for 3D Human Motion Estimation. In CVPR, 2005.
26. T. Starner and A. Pentland. Real-time ASL recognition from video using Hidden Markov Models. In ISCV, 1995.
27. C. Stauffer and E. Grimson. Learning patterns of activity using real-time tracking. In PAMI, 2000.
28. A. Torralba, K. Murphy, and W. Freeman. Contextual models for object detection using boosted random fields. In NIPS, 2004.
29. C. Vogler and D. Metaxas. A framework for recognizing the simultaneous aspects of ASL. In CVIU, 2001.