Hidden Markov Model on a unit hypersphere space for gesture trajectory recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Beh, Jounghoon | - |
dc.contributor.author | Han, David K. | - |
dc.contributor.author | Durasiwami, Ramani | - |
dc.contributor.author | Ko, Hanseok | - |
dc.date.accessioned | 2021-09-05T12:17:23Z | - |
dc.date.available | 2021-09-05T12:17:23Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2014-01-15 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/99532 | - |
dc.description.abstract | In this paper, a Mixture of von Mises-Fisher (MvMF) Probability Density Function (PDF) is incorporated into a Hidden Markov Model (HMM) in order to model spatio-temporal data in a unit-hypersphere space. The parameter estimation formulae for MvMF-HMM are derived in a closed form. As an application for the proposed MvMF-HMM, hands gesture trajectory recognition task is considered. Modeling gesture trajectory on a unit-hypersphere inherently removes bias from a subject's arm length or distance between a subject and camera. In experiments with public datasets, InteractPlay and UCF Kinect, the proposed MvMF-HMM showed superior recognition performance compared to current state-of-the-art techniques. (C) 2013 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | MOTION | - |
dc.title | Hidden Markov Model on a unit hypersphere space for gesture trajectory recognition | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Hanseok | - |
dc.identifier.doi | 10.1016/j.patrec.2013.10.007 | - |
dc.identifier.scopusid | 2-s2.0-84893049119 | - |
dc.identifier.wosid | 000329145400018 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.36, pp.144 - 153 | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 36 | - |
dc.citation.startPage | 144 | - |
dc.citation.endPage | 153 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | MOTION | - |
dc.subject.keywordAuthor | Directional statistics | - |
dc.subject.keywordAuthor | Gesture recognition | - |
dc.subject.keywordAuthor | Hidden Markov model | - |
dc.subject.keywordAuthor | Von Mises-Fisher distribution | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.