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 algorithmto a home video surveillance application and demonstrate its efficacy.
Cite
Text
Truyen et al. "AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.49Markdown
[Truyen et al. "AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/truyen2006cvpr-adaboost/) doi:10.1109/CVPR.2006.49BibTeX
@inproceedings{truyen2006cvpr-adaboost,
title = {{AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition}},
author = {Truyen, Tran The and Phung, Dinh Q. and Venkatesh, Svetha and Bui, Hung Hai},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {1686-1693},
doi = {10.1109/CVPR.2006.49},
url = {https://mlanthology.org/cvpr/2006/truyen2006cvpr-adaboost/}
}