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https://hdl.handle.net/10356/4747
Title: | Human activity recognition based on hidden Markov models | Authors: | Liu, Xiao Hui | Keywords: | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
Issue Date: | 2006 | Source: | Liu, X. H. (2006). Human activity recognition based on hidden Markov models. Doctoral thesis, Nanyang Technological University, Singapore. | Abstract: | 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. | URI: | https://hdl.handle.net/10356/4747 | DOI: | 10.32657/10356/4747 | Schools: | School of Electrical and Electronic Engineering | Rights: | Nanyang Technological University | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Theses |
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EEE-THESES_750.pdf | 7.87 MB | Adobe PDF | View/Open |
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