dc.contributor.authorLiu, Xiao Huien_US
dc.identifier.citationLiu, X. H. (2006). Human activity recognition based on hidden Markov models. Doctoral thesis, Nanyang Technological University, Singapore.
dc.description.abstractThis thesis discusses the main issues of human activity recognition systems, including automatic human activity segmentation, non-meaningful activity rejection and multi-agent activity recognition, and presents the contribution of this project for these issues. Three contributions are presented in this thesis. Firstly, a background-state based auto-segmentation framework is proposed to segment human activities of interest from continuous input. Secondly, the non-meaningful activities is rejected be a pairwise likelihood ratio test (PLRT), which has a good performance while only relying on information of meaningful patterns. Thirdly, an observation decomposed hidden Markov model (ODHMM) is proposed to recognize multi-agent activities, where the role of each agent can be identified automatically. These contributions concerned on various important aspects of human activity recognition and make it possible to build a real-life system.en_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
dc.titleHuman activity recognition based on hidden Markov modelsen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.supervisorChua Chin Sengen_US
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en_US

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