Human activity recognition based on hidden Markov models

This 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 t...

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Main Author: Liu, Xiao Hui
Other Authors: Chua Chin Seng
Format: Theses and Dissertations
Published: 2008
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Online Access:https://hdl.handle.net/10356/4747
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-47472023-07-04T16:47:36Z Human activity recognition based on hidden Markov models Liu, Xiao Hui Chua Chin Seng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering This 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. DOCTOR OF PHILOSOPHY (EEE) 2008-09-17T09:57:45Z 2008-09-17T09:57:45Z 2006 2006 Thesis Liu, X. H. (2006). Human activity recognition based on hidden Markov models. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/4747 10.32657/10356/4747 Nanyang Technological University application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
topic DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Liu, Xiao Hui
Human activity recognition based on hidden Markov models
description This 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.
author2 Chua Chin Seng
author_facet Chua Chin Seng
Liu, Xiao Hui
format Theses and Dissertations
author Liu, Xiao Hui
author_sort Liu, Xiao Hui
title Human activity recognition based on hidden Markov models
title_short Human activity recognition based on hidden Markov models
title_full Human activity recognition based on hidden Markov models
title_fullStr Human activity recognition based on hidden Markov models
title_full_unstemmed Human activity recognition based on hidden Markov models
title_sort human activity recognition based on hidden markov models
publishDate 2008
url https://hdl.handle.net/10356/4747
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