AdaBoost.MRF: boosted Markov random forests and application to multilevel activity recognition Tran, Truyen, Phung, Dinh Q., Bui, Hung H. and Venkatesh, Svetha 2006, AdaBoost.MRF: boosted Markov random forests and application to multilevel activity recognition, in CVPR 2006 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Piscataway, N.J., pp. 1686-1693, doi: 10.1109/CVPR.2006.49. Attached Files Name Description MIMEType Size Downloads venkatesh-adaboostmrf-2006.pdf Published version application/pdf 253.26KB 310 Title AdaBoost.MRF: boosted Markov random forests and application to multilevel activity recognition Author(s) Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Dinh Q. orcid.org/0000-0002-9977-8247 Bui, Hung H. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Computer Vision and Pattern Recognition. Conference (2006 : New York, N.Y.) Conference location New York, N.Y. Conference dates 17-22 Jun. 2006 Title of proceedings CVPR 2006 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Editor(s) [Unknown] Publication date 2006 Conference series Computer Vision and Pattern Recognition. Conference Start page 1686 End page 1693 Total pages 8 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) artificial intelligence computer vision convergence hidden Markov models inference algorithms intelligent structures intelligent systems Markov random fields maximum likelihood estimation parameter estimation Summary Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to a home video surveillance application and demonstrate its efficacy. 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 9780769525976 0769525970 Language eng DOI 10.1109/CVPR.2006.49 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:30044600 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     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, 11:34:36 EST Fri, 27 Apr 2012, 14:24:28 EST Fri, 27 Apr 2012, 14:25:16 EST Fri, 27 Apr 2012, 14:26:14 EST Thu, 03 May 2012, 11:25:18 EST Fri, 04 Mar 2016, 07:39:01 EST Fri, 04 Mar 2016, 07:46:06 EST Tue, 06 Sep 2016, 18:44:59 EST Tue, 05 Dec 2017, 12:31:29 EST Fri, 07 Sep 2018, 15:52:34 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 14 times in Scopus Search Google Scholar Access Statistics: 963 Abstract Views, 310 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:34:36 EST

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