dc.contributor.authorTao, Meng.en_US
dc.date.accessioned2008-09-17T09:29:38Z
dc.date.available2008-09-17T09:29:38Z
dc.date.copyright2007en_US
dc.date.issued2007
dc.identifier.citationTao, M. (2007). Human pose estimation based on data-driven Monte Carlo hidden Markov models. Master’s thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/3416
dc.description.abstractEstimating human poses from 2D images or video sequences can provide the moving trajectories of the body joints for the high level processing, human activity recognition, which is applicable in surveillance, human-computer interaction and clinical and sport analysis. This project proposes a new statistical formulation called the data-driven Monte Carlo hidden Markov model to estimate human poses from random initializations.en_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
dc.titleHuman pose estimation based on data-driven Monte Carlo hidden Markov models.en_US
dc.typeThesisen_US
dc.contributor.supervisorChua, Chin Seng.en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMASTER OF ENGINEERING (EEE)en_US


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