AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition

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.

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh, Hung Hai Bui
• Keywords: Adaboost.mrf Algorithm, Computer Vision, Conditional Random Field, CVPR 2006, Efficient Boosting-based Algorithm, Intelligent Monitoring Systems, Markov Random Field