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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.

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Title AdaBoost.MRF: boosted Markov random forests and application to multilevel activity recognition
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
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 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: School of Information Technology
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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.