Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, In Hwan | - |
dc.contributor.author | Pak, Jung Min | - |
dc.contributor.author | Ahn, Choon Ki | - |
dc.contributor.author | Lee, Seung Han | - |
dc.contributor.author | Lim, Myo Taeg | - |
dc.contributor.author | Song, Moon Kyou | - |
dc.date.accessioned | 2021-09-04T11:03:49Z | - |
dc.date.available | 2021-09-04T11:03:49Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2015-11 | - |
dc.identifier.issn | 0263-2241 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/92053 | - |
dc.description.abstract | This 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.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | FUZZY INFERENCE SYSTEM | - |
dc.subject | KALMAN FILTER | - |
dc.subject | STATE-SPACE | - |
dc.subject | MEMORY | - |
dc.subject | PERFORMANCE | - |
dc.subject | MODELS | - |
dc.subject | NOISE | - |
dc.title | Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
dc.contributor.affiliatedAuthor | Lim, Myo Taeg | - |
dc.identifier.doi | 10.1016/j.measurement.2015.07.020 | - |
dc.identifier.scopusid | 2-s2.0-84940521211 | - |
dc.identifier.wosid | 000361248900034 | - |
dc.identifier.bibliographicCitation | MEASUREMENT, v.75, pp.338 - 353 | - |
dc.relation.isPartOf | MEASUREMENT | - |
dc.citation.title | MEASUREMENT | - |
dc.citation.volume | 75 | - |
dc.citation.startPage | 338 | - |
dc.citation.endPage | 353 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | FUZZY INFERENCE SYSTEM | - |
dc.subject.keywordPlus | KALMAN FILTER | - |
dc.subject.keywordPlus | STATE-SPACE | - |
dc.subject.keywordPlus | MEMORY | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | NOISE | - |
dc.subject.keywordAuthor | Visual object tracking | - |
dc.subject.keywordAuthor | Finite impulse response (FIR) filter | - |
dc.subject.keywordAuthor | Optical flow | - |
dc.subject.keywordAuthor | Adaptive neuro fuzzy inference system (ANFIS) | - |
dc.subject.keywordAuthor | ANFIS-based FIR filter and OF arbitration (AFOA) | - |
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