AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition
作者： Tran The Truyen , D.Q. Phung , S. Venkatesh , H.H. Bui
DOI: 10.1109/CVPR.2006.49
关键词:
摘要: Activity recognition is an important issue in building intelligent monitoring systems. We address the of multilevel activities this paper via a conditional Markov random field (MRF), known as dynamic (DCRF). Parameter estimation general MRFs using maximum likelihood to be computationally challenging (except for extreme cases), and thus we propose efficient boosting-based algorithm AdaBoost.MRF task. Distinct from most existing work, our can handle hidden variables (missing labels) particularly attractive smarthouse domains where reliable labels are often sparsely observed. Furthermore, method works exclusively on trees guaranteed converge. apply algorithmto home video surveillance application demonstrate its efficacy.
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