Factored State-Abstract Hidden Markov Models for activity recognition using pervasive multi-modal sensors

Proceedings of the 2005 Intelligent Sensors, Sensor Networks and Information Processing Conference

Volumn 2005, Issue , 2005, Pages 331-335

  Tran, Dung T ^a   Phung, Dinh Q ^a   Bui, Hung H ^a   Venkatesh, Svetha ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CAMERAS; DATA ACQUISITION; MARKOV PROCESSES; MATHEMATICAL MODELS; SENSORS; STATE SPACE METHODS;

ABSTRACT HIDDEN MARKOV MODEL (AHMM); ACTIVITY RECOGNITION; INTEGRATED SENSOR INFORMATION; STATE SPACE EXPLOSION;

PATTERN RECOGNITION;

EID: 33847195040     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/issnip.2005.1595601     Document Type: Conference Paper

References (17)

1. C. Boutilier, R. Dearden, and M. Goldszmidt. Stochastic dynamic programming with factored representations. Artificial Intelligence, 121(1-2):49-107, 2000.
2. C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller. Context-specific independence in Bayesian networks. In Uncertainty in Artificial Intelligence, pages 115-123, 1996.
3. M. Brand, N. Oliver, and A. Pentland. Coupled hidden markov models for complex action recognition. In CVPR '97: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), page 994, 1997.
4. H. H. Bui. Abstract hidden markov models for online probabilistic plan recognition. 2001.
5. H. H. Bui, S. Venkatesh, and G. West. Policy recognition in the abstract hidden markov model. Artificial Intelligence Research, 17:451-499, 2002.
6. S. Fine, Y. Singer, and N. Tishby. The hierarchical hidden markov model: Analysis and applications. In Machine Learning, number 32, pages 41-64, 1999.
7. A. Galata, N. Johnson, and D. Hogg. Learning variable length markov models of behaviour, 2001.
8. C. D. Kidd, R. Orr, G. D. Abowd, C. G. Atkeson, I. A. Essa, B. Mac-Intyre, E. D. Mynatt, T. Starner, and W. Newstetter. The aware home: A living laboratory for ubiquitous computing research. In Cooperative Buildings, pages 191-198, 1999.
9. L. Liao, D. Fox, and H. Kautz. Learning and inferring transportation routines. In National Conference on Artificial Intelligence (AAAI-04), pages 880-887, 2004.
10. K. P. Murphy. Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, July, 2002.
11. K. P. Murphy. Hierarchical hmms, November 2001.
12. N. T. Nguyen, H. H. Bui, S. Venkatesh, and G. West. Recognising and monitoring high-level behaviours in complex spatial environments. 2003.
13. N. Oliver, E. Horvitz, and A. Garg. Layered representations for human activity recognition. In ICMI '02: Proceedings of the 4th IEEE International Conference on Multimodal Interfaces, page 3. IEEE Computer Society, 2002.
14. S. Osentoski, V. Manfredi, and S. Mahadevan. Learning hierarchical models of activity. In IEEE/RSJ International Conference on Robots and Systems (IROS). (to appear), 2004.
15. D. Patterson, L. Liao, D. Fox, and H. Kautz. Inferring high-level behavior from low-level sensors, 2003.
16. V. Stanford. Using pervasive computing to deliver elder care. IEEE Pervasive Computing, 1(1):10-13, 2002.
17. J. Yin, X. Chai, and Q. Yang. High-level goal recognition in a wireless lan. In Nineteenth National Conference on Artificial Intelligence (AAAI 04), San Jose, CA USA, July, 2004.