AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition

Tran The Truyen; Dinh Q. Phung; Hung H. Bui; Svetha Venkatesh

doi:10.1109/CVPR.2006.49

AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition

Tran The Truyen, Dinh Q. Phung, Hung H. Bui, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

15 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages	1686-1693
Number of pages	8
DOIs	https://doi.org/10.1109/CVPR.2006.49
Publication status	Published - 22 Dec 2006
Externally published	Yes
Event	IEEE Conference on Computer Vision and Pattern Recognition 2006 - New York, United States of America Duration: 17 Jun 2006 → 22 Jun 2006 https://ieeexplore.ieee.org/xpl/conhome/10924/proceeding?isnumber=34373 (Proceedings)

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	2
ISSN (Print)	1063-6919

Conference

Conference	IEEE Conference on Computer Vision and Pattern Recognition 2006
Abbreviated title	CVPR 2006
Country/Territory	United States of America
City	New York
Period	17/06/06 → 22/06/06
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10924/proceeding?isnumber=34373 (Proceedings)

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10.1109/CVPR.2006.49

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Truyen, T. T., Phung, D. Q., Bui, H. H., & Venkatesh, S. (2006). AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition. In Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 (pp. 1686-1693). (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2). https://doi.org/10.1109/CVPR.2006.49

Truyen, Tran The ; Phung, Dinh Q. ; Bui, Hung H. ; Venkatesh, Svetha. / AdaBoost.MRF : Boosted Markov random forests and application to multilevel activity recognition. Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006. 2006. pp. 1686-1693 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

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title = "AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition",

abstract = "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.",

author = "Truyen, {Tran The} and Phung, {Dinh Q.} and Bui, {Hung H.} and Svetha Venkatesh",

year = "2006",

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note = "IEEE Conference on Computer Vision and Pattern Recognition 2006, CVPR 2006 ; Conference date: 17-06-2006 Through 22-06-2006",

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Truyen, TT, Phung, DQ, Bui, HH & Venkatesh, S 2006, AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition. in Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1686-1693, IEEE Conference on Computer Vision and Pattern Recognition 2006, New York, New York, United States of America, 17/06/06. https://doi.org/10.1109/CVPR.2006.49

AdaBoost.MRF : Boosted Markov random forests and application to multilevel activity recognition. / Truyen, Tran The; Phung, Dinh Q.; Bui, Hung H.; Venkatesh, Svetha.

Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006. 2006. p. 1686-1693 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - 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.

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Truyen TT, Phung DQ, Bui HH, Venkatesh S. AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition. In Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006. 2006. p. 1686-1693. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2006.49

AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition

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