Strategies for inference mechanism of conditional random fields for multiple-resident activity recognition in a smart home

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6096 LNAI, Issue PART 1, 2010, Pages 417-426

  Hsu, Kuo Chung ^a   Chiang, Yi Ting ^a   Lin, Gu Yang ^a   Lu, Ching Hu ^a   Hsu, Jane Yung Jen ^a   Fu, Li Chen ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

activity recognition; conditional random field (CRF); data association; multiple residents; Smart home

Indexed keywords

ACTIVITY RECOGNITION; CONDITIONAL RANDOM FIELD (CRF); DATA ASSOCIATION; MULTIPLE RESIDENTS; SMART HOMES;

INDUSTRIAL ENGINEERING; INTELLIGENT SYSTEMS;

IMAGE SEGMENTATION;

EID: 79551525754     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13022-9_42     Document Type: Conference Paper

References (17)

1. Wang, L., Gu, T., Tao, X., Lu, J.: Sensor-Based Human Activity Recognition in a Multiuser Scenario. In: Ambient Intelligence, pp. 78-87 (2009)
2. McCowan, L., Gatica-Perez, D., Bengio, S., Lathoud, G., Barnard, M., Zhang, D.: Automatic analysis of multimodal group actions in meetings. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 305-317 (2005)
3. Nguyen, N., Venkatesh, S., Bui, H.: Recognising Behaviours of Multiple People with Hierarchical Probabilistic Model and Statistical Data Association. Presented at the British Machine Vision Conference (2005)
4. Wilson, D.H., Atkeson, C.G.: Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors. Presented at the Pervasive (2005)
5. Singla, G., Cook, D., Schmitter-Edgecombe, M.: Recognizing independent and joint activities among multiple residents in smart environments. Ambient Intelligence and Humanized Computing Journal (2009)
6. Lu, C.-H., Wu, C.-L., Fu, L.-C.: Hide and Not Easy to Seek: A Hybrid Weaving Strategy for Context-aware Service Provision in a Smart Home. Presented at the IEEE Asia-Pacific Services Computing Conference, Yilan, Taiwan (2008)
7. Kasteren, T.v., Noulas, A., Englebienne, G., Krose, B.: Accurate activity recognition in a home setting. Presented at the Proceedings of the 10th international conference on Ubiquitous computing, Seoul, Korea (2008)
8. Ye, J., Clear, A.K., Coyle, L., Dobson, S.: On using temporal features to create more accurate human-activity classifiers. Presented at the 20th Conference on Artificial Intelligence and Cognitive Science, UCD Dublin, Ireland (2009)
9. Chen, Y.-H., Lu, C.-H., Hsu, K.-C., Fu, L.-C.: Preference Model Assisted Activity Recognition Learning in a Smart Home Environment. Presented at the International Conference on Intelligent Robots and Systems, St. Louis, MO, USA (2009)
10. W.S. University: CASAS Smart Home Project, http://ailab.eecs.wsu.edu/ casas/
11. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Presented at the ICML (2001)
12. Vail, D.L., Veloso, M.M., Lafferty, J.D.: Conditional random fields for activity recognition. In: International Conference on Autonomous Agents and Multi-agent Systems (2007)
13. Lian, C.-C., Hsu, J.Y.-J.: Probabilistic Models for Concurrent Chatting Activity Recognition. Presented at the Proceedings of IJCAI-2009, Pasadena (2009)
14. Shewchuk, J.R.: An Introduction to the Conjugate Gradient Method Without the Agonizing Pain (1994)
15. Darroch, J.N., Ratcliff, D.: Generalized iterative scaling for log-linear models. Presented at the Annals of Mathematical Statistics (1972)
16. Nocedal, J.: Updating Quasi-Newton Matrices with Limited Storage. Mathematics of Computation 35, 773-782 (1980)
17. Kudo, T.: CRF++: Yet Another CRF toolkit (2007), http://crfpp. sourceforge.net/