DocumentCode :
2086518
Title :
AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition
Author :
Truyen, Tran The ; Phung, Dinh Q. ; Venkatesh, Svetha ; Bui, Hung H.
Author_Institution :
Curtin University of Technology
Volume :
2
fYear :
2006
fDate :
2006
Firstpage :
1686
Lastpage :
1693
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 algorithmto a home video surveillance application and demonstrate its efficacy.
Keywords :
Artificial intelligence; Computer vision; Convergence; Hidden Markov models; Inference algorithms; Intelligent structures; Intelligent systems; Markov random fields; Maximum likelihood estimation; Parameter estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2597-0
Type :
conf
DOI :
10.1109/CVPR.2006.49
Filename :
1640958
Link To Document :
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