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Conference Paper: A new visual object tracking algorithm using Bayesian Kalman filter

TitleA new visual object tracking algorithm using Bayesian Kalman filter
Authors
KeywordsObject tracking
Baysian Kalman filter
Mean shift
Issue Date2014
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089
Citation
The 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia, 1-5 June 2014. In IEEE International Symposium on Circuits and Systems Proceedings, 2014, p. 522-525 How to Cite?
AbstractThis paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is also proposed. Experimental results show that our method can successfully handle complex scenarios with good performance and low arithmetic complexity. © IEEE
Persistent Identifierhttp://hdl.handle.net/10722/204109
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorZhang, Sen_US
dc.contributor.authorChan, SCen_US
dc.contributor.authorLiao, Ben_US
dc.contributor.authorTsui, KMen_US
dc.date.accessioned2014-09-19T20:06:06Z-
dc.date.available2014-09-19T20:06:06Z-
dc.date.issued2014en_US
dc.identifier.citationThe 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia, 1-5 June 2014. In IEEE International Symposium on Circuits and Systems Proceedings, 2014, p. 522-525en_US
dc.identifier.isbn978-1-4799-3432-4-
dc.identifier.issn0271-4302-
dc.identifier.urihttp://hdl.handle.net/10722/204109-
dc.description.abstractThis paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is also proposed. Experimental results show that our method can successfully handle complex scenarios with good performance and low arithmetic complexity. © IEEE-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089en_US
dc.relation.ispartofIEEE International Symposium on Circuits and Systems Proceedingsen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_US
dc.rights©2014 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.en_US
dc.subjectObject tracking-
dc.subjectBaysian Kalman filter-
dc.subjectMean shift-
dc.titleA new visual object tracking algorithm using Bayesian Kalman filteren_US
dc.typeConference_Paperen_US
dc.identifier.emailChan, SC: ascchan@hku.hken_US
dc.identifier.emailLiao, B: binliao@hku.hken_US
dc.identifier.emailTsui, KM: kmtsui11@hku.hken_US
dc.identifier.authorityChan, SC=rp00094en_US
dc.identifier.authorityTsui, KM=rp00181en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISCAS.2014.6865187en_US
dc.identifier.hkuros239784en_US
dc.identifier.spage522en_US
dc.identifier.epage525en_US
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 141006-

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