Visual Tracking Using Wang-Landau Reinforcement Sampler
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, Dokyeong | - |
dc.contributor.author | Kwon, Junseok | - |
dc.date.available | 2021-03-06T04:40:19Z | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/43770 | - |
dc.description.abstract | In this study, we present a novel tracking system, in which the tracking accuracy can be considerably enhanced by state prediction. Accordingly, we present a new Q-learning-based reinforcement method, augmented by Wang-Landau sampling. In the proposed method, reinforcement learning is used to predict a target configuration for the subsequent frame, while Wang-Landau sampler balances the exploitation and exploration degrees of the prediction. Our method can adapt to control the randomness of policy, using statistics on the number of visits in a particular state. Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance. Numerical results demonstrate that our method substantially outperforms other state-of-the-art visual trackers and runs in realtime because our method contains no complicated deep neural network architectures. | - |
dc.format.extent | 17 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Visual Tracking Using Wang-Landau Reinforcement Sampler | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/app10217780 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.10, no.21, pp 1 - 17 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000588987400001 | - |
dc.identifier.scopusid | 2-s2.0-85095758824 | - |
dc.citation.endPage | 17 | - |
dc.citation.number | 21 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 10 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | Wang&#8211 | - |
dc.subject.keywordAuthor | Landau Monte Carlo | - |
dc.subject.keywordAuthor | reinforcement learning | - |
dc.subject.keywordAuthor | 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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