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 |
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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 | |
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 |
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Volume | 2 |
ISSN (Print) | 1063-6919 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2006 |
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Abbreviated title | CVPR 2006 |
Country/Territory | United States of America |
City | New York |
Period | 17/06/06 → 22/06/06 |
Internet address |