Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking
DC Field | Value | Language |
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dc.contributor.author | Jeong, Soowoong | - |
dc.contributor.author | Paik, Joonki | - |
dc.date.available | 2019-03-07T04:36:15Z | - |
dc.date.issued | 2018-08 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1933 | - |
dc.description.abstract | In visual object tracking, the dynamic environment is a challenging issue. Partial occlusion and scale variation are typical challenging problems. We present a correlation-based object tracking based on the discriminative model. To attenuate the influence by partial occlusion, partial sub-blocks are constructed from the original block, and each of them operates independently. The scale space is employed to deal with scale variation using a feature pyramid. We also present an adaptive update model with a weighting function to calculate the frame-adaptive learning rate. Theoretical analysis and experimental results demonstrate that the proposed method can robustly track drastic deformed objects. The sparse update reduces the computational cost for real-time tracking. Although the partial block scheme generation increases the computational cost, we present a novel sparse update approach to reduce the computational cost drastically for real-time tracking. The experiments were performed on a variety of sequences, and the proposed method exhibited better performance compared with the state-of-the-art trackers. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/app8081349 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.8, no.8 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000442864900139 | - |
dc.identifier.scopusid | 2-s2.0-85051502819 | - |
dc.citation.number | 8 | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 8 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | computer vision | - |
dc.subject.keywordAuthor | object tacking | - |
dc.subject.keywordAuthor | correlation filter | - |
dc.subject.keywordAuthor | partial block | - |
dc.subject.keywordAuthor | scale space | - |
dc.subject.keywordAuthor | adaptive learning | - |
dc.subject.keywordAuthor | discriminative model | - |
dc.subject.keywordAuthor | partial occlusion | - |
dc.subject.keywordAuthor | scale variation | - |
dc.subject.keywordPlus | ROBUST VISUAL TRACKING | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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