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Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking

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dc.contributor.authorChoi, In Hwan-
dc.contributor.authorPak, Jung Min-
dc.contributor.authorAhn, Choon Ki-
dc.contributor.authorLee, Seung Han-
dc.contributor.authorLim, Myo Taeg-
dc.contributor.authorSong, Moon Kyou-
dc.date.accessioned2021-09-04T11:03:49Z-
dc.date.available2021-09-04T11:03:49Z-
dc.date.created2021-06-10-
dc.date.issued2015-11-
dc.identifier.issn0263-2241-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/92053-
dc.description.abstractThis paper proposes a new visual object tracking algorithm based on an adaptive neuro fuzzy inference system (ANFIS) for arbitration algorithm between a finite impulse response (FIR) filter and optical flow (OF). The proposed algorithm is called ANFIS-based FIR filter and OF arbitration (AFOA). The AFOA operates as an FIR filter for normal situations, keeping the computational cost low, and, when abrupt turns occur, converts to an OF to compensate for the inaccuracy of the FIR filter. An ANFIS-based arbitration algorithm constructs a mapping system from given inputs to an output using fuzzy logic and determines tracking mode of the tracking process between the FIR filter and the OF. The effectiveness of the AFOA algorithm is demonstrated by experiments employed on real-time video clips along with a comparative analysis with the ANFIS-based Kalman filter and OF arbitration (AKOA). (C) 2015 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCI LTD-
dc.subjectFUZZY INFERENCE SYSTEM-
dc.subjectKALMAN FILTER-
dc.subjectSTATE-SPACE-
dc.subjectMEMORY-
dc.subjectPERFORMANCE-
dc.subjectMODELS-
dc.subjectNOISE-
dc.titleArbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking-
dc.typeArticle-
dc.contributor.affiliatedAuthorAhn, Choon Ki-
dc.contributor.affiliatedAuthorLim, Myo Taeg-
dc.identifier.doi10.1016/j.measurement.2015.07.020-
dc.identifier.scopusid2-s2.0-84940521211-
dc.identifier.wosid000361248900034-
dc.identifier.bibliographicCitationMEASUREMENT, v.75, pp.338 - 353-
dc.relation.isPartOfMEASUREMENT-
dc.citation.titleMEASUREMENT-
dc.citation.volume75-
dc.citation.startPage338-
dc.citation.endPage353-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaInstruments & Instrumentation-
dc.relation.journalWebOfScienceCategoryEngineering, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryInstruments & Instrumentation-
dc.subject.keywordPlusFUZZY INFERENCE SYSTEM-
dc.subject.keywordPlusKALMAN FILTER-
dc.subject.keywordPlusSTATE-SPACE-
dc.subject.keywordPlusMEMORY-
dc.subject.keywordPlusPERFORMANCE-
dc.subject.keywordPlusMODELS-
dc.subject.keywordPlusNOISE-
dc.subject.keywordAuthorVisual object tracking-
dc.subject.keywordAuthorFinite impulse response (FIR) filter-
dc.subject.keywordAuthorOptical flow-
dc.subject.keywordAuthorAdaptive neuro fuzzy inference system (ANFIS)-
dc.subject.keywordAuthorANFIS-based FIR filter and OF arbitration (AFOA)-
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공과대학 (전기전자공학부)
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