Maximum likelihood FIR filter for visual object tracking
DC Field | Value | Language |
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dc.contributor.author | Pak, Jung Min | - |
dc.contributor.author | Ahn, Choon Ki | - |
dc.contributor.author | Mo, Yung Hak | - |
dc.contributor.author | Lim, Myo Taeg | - |
dc.contributor.author | Song, Moon Kyou | - |
dc.date.accessioned | 2021-09-03T15:57:51Z | - |
dc.date.available | 2021-09-03T15:57:51Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2016-12-05 | - |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/86566 | - |
dc.description.abstract | Visual object trackers usually adopt filters, such as the Kalman filter (KF) and the particle filter (PF), in order to improve tracking accuracy by suppressing measurement noises. However, if the filters have infinite impulse response (IIR) structures, the visual trackers adopting them can exhibit degraded tracking performance when system models have parameter uncertainties or when the noise information is incorrect. To overcome this problem, in this paper, we propose a new finite impulse response (FIR) filter for visual object tracking (VOT). The proposed filter is derived by maximizing the likelihood function, and it is referred to as the maximum likelihood FIR filter (MLFIRF). We conducted extensive experiments to show that the MLFIRF provides superior and more reliable tracking results compared with the KF, PF, and H-infinity filter (HF) in VOT. 2016 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | ALGORITHM | - |
dc.subject | MEMORY | - |
dc.title | Maximum likelihood FIR filter 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.neucom.2016.07.047 | - |
dc.identifier.scopusid | 2-s2.0-84994121203 | - |
dc.identifier.wosid | 000388777400051 | - |
dc.identifier.bibliographicCitation | NEUROCOMPUTING, v.216, pp.543 - 553 | - |
dc.relation.isPartOf | NEUROCOMPUTING | - |
dc.citation.title | NEUROCOMPUTING | - |
dc.citation.volume | 216 | - |
dc.citation.startPage | 543 | - |
dc.citation.endPage | 553 | - |
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 | ALGORITHM | - |
dc.subject.keywordPlus | MEMORY | - |
dc.subject.keywordAuthor | Finite impulse response (FIR) filter | - |
dc.subject.keywordAuthor | Maximum likelihood | - |
dc.subject.keywordAuthor | Maximum likelihood FIR filter (MLFIRF) | - |
dc.subject.keywordAuthor | Visual object tracking (VOT) | - |
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