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. Attached Files Name Description MIMEType Size Downloads phung-aprobabilisticmodel-2006.pdf Published version application/pdf 166.83KB 78 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.org/0000-0002-9977-8247 Bui, Hung H. 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   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 20 Apr 2012, 11:34:40 EST Fri, 27 Apr 2012, 13:14:47 EST Fri, 27 Apr 2012, 13:17:29 EST Thu, 03 May 2012, 11:28:23 EST Tue, 28 Jan 2014, 13:36:57 EST Tue, 06 Sep 2016, 18:45:06 EST Sat, 21 Oct 2017, 02:04:08 EST Tue, 05 Dec 2017, 12:31:29 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 382 Abstract Views, 78 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:34:40 EST

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