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Conference Paper: A self-growing Bayesian network classifier for online learning of human motion patterns

TitleA self-growing Bayesian network classifier for online learning of human motion patterns
Authors
KeywordsBayesian network classifier
Human motion patterns
Online learning
Issue Date2010
PublisherIEEE.
Citation
The 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR 2010), Paris, France, 7-10 December 2010. In Proceedings of SoCPaR2010, 2010, p. 182-187 How to Cite?
AbstractThis paper proposes a new self-growing Bayesian network classifier for online learning of human motion patterns (HMPs) in dynamically changing environments. The proposed classifier is designed to represent HMP classes based on a set of historical trajectories labeled by unsupervised clustering. It then assigns HMP class labels to current trajectories. Parameters of the proposed classifier are recalculated based on the augmented dataset of labeled trajectories and all HMP classes are accordingly updated. As such, the proposed classifier allows current trajectories to form new HMP classes when they are sufficiently different from existing HMP classes. The performance of the proposed classifier is evaluated by a set of real-world data. The results show that the proposed classifier effectively learns new HMP classes from current trajectories in an online manner. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/137724
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorChen, Zen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2011-08-26T14:32:27Z-
dc.date.available2011-08-26T14:32:27Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR 2010), Paris, France, 7-10 December 2010. In Proceedings of SoCPaR2010, 2010, p. 182-187en_HK
dc.identifier.isbn978-1-4244-7896-5en_US
dc.identifier.urihttp://hdl.handle.net/10722/137724-
dc.description.abstractThis paper proposes a new self-growing Bayesian network classifier for online learning of human motion patterns (HMPs) in dynamically changing environments. The proposed classifier is designed to represent HMP classes based on a set of historical trajectories labeled by unsupervised clustering. It then assigns HMP class labels to current trajectories. Parameters of the proposed classifier are recalculated based on the augmented dataset of labeled trajectories and all HMP classes are accordingly updated. As such, the proposed classifier allows current trajectories to form new HMP classes when they are sufficiently different from existing HMP classes. The performance of the proposed classifier is evaluated by a set of real-world data. The results show that the proposed classifier effectively learns new HMP classes from current trajectories in an online manner. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE.en_US
dc.relation.ispartofProceedings of the International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010en_HK
dc.rightsProceedings of the International Conference of Soft Computing and Pattern Recognition. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectBayesian network classifieren_HK
dc.subjectHuman motion patternsen_HK
dc.subjectOnline learningen_HK
dc.titleA self-growing Bayesian network classifier for online learning of human motion patternsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-7896-5&volume=&spage=182&epage=187&date=2010&atitle=A+self-growing+Bayesian+network+classifier+for+online+learning+of+human+motion+patternsen_US
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/SOCPAR.2010.5686087en_HK
dc.identifier.scopuseid_2-s2.0-79951468775en_HK
dc.identifier.hkuros191010en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79951468775&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage182en_HK
dc.identifier.epage187en_HK
dc.description.otherThe 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR 2010), Paris, France, 7-10 December 2010. In Proceedings of SoCPaR2010, 2010, p. 182-187-
dc.identifier.scopusauthoridChen, Z=35228484900en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK

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