venkatesh-adaboostmrf-2006.pdf (253.26 kB)
AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition
conference contribution
posted on 2006-01-01, 00:00 authored by Truyen TranTruyen Tran, Quoc-Dinh Phung, H Bui, Svetha VenkateshSvetha VenkateshActivity 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.
History
Event
Computer Vision and Pattern Recognition. Conference (2006 : New York, N.Y.)Pagination
1686 - 1693Publisher
IEEELocation
New York, N.Y.Place of publication
Piscataway, N.J.Start date
2006-06-17End date
2006-06-22ISBN-13
9780769525976ISBN-10
0769525970Language
engNotes
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E1.1 Full written paper - refereedCopyright notice
2006, IEEETitle of proceedings
CVPR 2006 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern RecognitionUsage metrics
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