AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition

Tran The Truyen, Dinh Q. Phung, Hung H. Bui, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

14 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages1686-1693
Number of pages8
DOIs
Publication statusPublished - 22 Dec 2006
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2006 - New York, United States of America
Duration: 17 Jun 200622 Jun 2006
https://ieeexplore.ieee.org/xpl/conhome/10924/proceeding?isnumber=34373 (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
ISSN (Print)1063-6919

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2006
Abbreviated titleCVPR 2006
CountryUnited States of America
CityNew York
Period17/06/0622/06/06
Internet address

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