dc.contributor.authorLiu, Xiao Huien_US
dc.date.accessioned2008-09-17T09:57:45Z
dc.date.accessioned2017-07-23T08:31:59Z
dc.date.available2008-09-17T09:57:45Z
dc.date.available2017-07-23T08:31:59Z
dc.date.copyright2006en_US
dc.date.issued2006
dc.identifier.citationLiu, X. H. (2006). Human activity recognition based on hidden Markov models. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/4747
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.typeThesisen_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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