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A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment

Tran, Dung T., Phung, Dinh Q., Bui, Hung H. and Venkatesh, Svetha 2006, A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment, in ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 168-172.

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Title A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment
Author(s) Tran, Dung T.
Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Bui, Hung H.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Pattern Recognition (18th : 2006 : Hong Kong, China)
Conference location Hong Kong, China
Conference dates 20-24 Aug. 2006
Title of proceedings ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2006
Conference series International Conference on Pattern Recognition
Start page 168
End page 172
Total pages 5
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) pervasive environment
recognizing activity
sensor fusion
state transition parameters
Summary To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0769525210
9780769525211
ISSN 1051-4651
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2006, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044601

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.