An online HDP-HMM for joint action segmentation and classification in motion capture data

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2012, Pages 1-7

  Bargi, Ava ^a   Da Xu, Richard Yi ^a   Piccardi, Massimo ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION RECOGNITION; DATA SETS; HDP-HMM; HIERARCHICAL DIRICHLET PROCESS; JOINT ACTIONS; JOINT SEGMENTATION; MOTION CAPTURE DATA; ONLINE METHODS; PREDEFINED SETS; STREAMING DATA; TIME SEGMENTATION;

DATA HANDLING; GESTURE RECOGNITION; HIDDEN MARKOV MODELS; MOTION ESTIMATION;

COMPUTER VISION;

EID: 84864965209     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2012.6239230     Document Type: Conference Paper

References (15)

1. K. R. Canini, L. Shi, and T. L. Griffiths. Online Inference of Topics with Latent Dirichlet Allocation. In Proc. AISTATS 2009, volume 5, pages 65-72, 2009.
2. E. Fox, E. Sudderth, M. Jordan, and A. Willsky. Developing a tempered HDP-HMM for systems with state persistence. Technical report, MIT Laboratory for Information and Decision Systems, 2007.
3. E. Fox, E. Sudderth, M. Jordan, and A. Willsky. Bayesian Nonparametric Inference of Switching Dynamic Linear Models. IEEE Transactions on Signal Processing, 59(4):1569-1585, 2011.
4. E. B. Fox. HDP-HMM toolbox, beta version, 2009.
5. E. B. Fox, E. B. Sudderth, M. I. Jordan, and A. S. Willsky. An HDP-HMM for systems with state persistence. In Proc. ICML, July 2008.
6. M. Hoai, Z. Lan, and F. De la Torre. Joint segmentation and classification of human actions in video. In Proc. CVPR, 2011.
7. T. Hospedales, S. Gong, and T. Xiang. A markov clustering topic model for mining behaviour in video. In Computer Vision, 2009 IEEE 12th International Conference on, pages 1165-1172, 29 2009-oct. 2 2009.
8. J. Sethuraman. A constructive definition of Dirichlet priors. Statistica Sinica, 4:639-650, 1994.
9. C. Sminchisescu, A. Kanaujia, and D. Metaxas. Conditional models for contextual human motion recognition. Computer Vision and Image Understanding, 104(2-3):210-220, 2006.
10. T. Taniguchi, K. Hamahata, and N. Iwahashi. Unsupervised segmentation of human motion data using a sticky hierarchical dirichlet process-hidden markov model and minimal description length-based chunking method for imitation learning. Advanced Robotics, 25(17):2143-2172, 2011.
11. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566-1581, 2006.
12. M. Tenorth, J. Bandouch, and M. Beetz. The TUM Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition. In IEEE International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences (THEMIS), in conjunction with ICCV2009, 2009.
13. D. L. Vail, M. M. Veloso, and J. D. Lafferty. Conditional Random Fields for Activity Recognition. In Proc. Int. Conf. on Autonomous Agents and Multi-Agent Systems, 2007.
14. J. Van Gael, Y. Saatci, Y. W. Teh, and Z. Ghahramani. Beam sampling for the infinite hidden Markov model. In Proc. ICML, pages 1088-1095. ACM, 2008.
15. J. Yamato, J. Ohya, and K. Ishii. Recognizing human action in time-sequential images using hidden Markov model. In Proc. CVPR, pages 379-385, 1992.