Human pose estimation based on data-driven Monte Carlo hidden Markov models

Human pose estimation based on data-driven Monte Carlo hidden Markov models

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 stat...

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Bibliographic Details
Main Author: Tao, Meng
Other Authors: Yau Wei Yun
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Computer science and engineering::Computing methodologies
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Online Access:https://hdl.handle.net/10356/3416
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Institution: Nanyang Technological University
Description
Summary: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.

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