Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/3416
Title: Human pose estimation based on data-driven Monte Carlo hidden Markov models
Authors: Tao, Meng
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Issue Date: 2007
Source: Tao, M. (2007). Human pose estimation based on data-driven Monte Carlo hidden Markov models. Master’s thesis, Nanyang Technological University, Singapore.
Abstract: Estimating 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.
URI: https://hdl.handle.net/10356/3416
DOI: 10.32657/10356/3416
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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