Particle swarm optimization–Markov Chain Monte Carlo for accurate visual tracking with adaptive template update
DC Field | Value | Language |
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dc.contributor.author | Kwon, Junseok | - |
dc.date.available | 2019-06-26T01:30:59Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 1568-4946 | - |
dc.identifier.issn | 1872-9681 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/26351 | - |
dc.description.abstract | A novel tracking method is proposed, which infers a target state and appearance template simultaneously. With this simultaneous inference, the method accurately estimates the target state and robustly updates the target template. The joint inference is performed by using the proposed particle swarm optimization–Markov chain Monte Carlo (PSO–MCMC) sampling method. PSO–MCMC is a combination of the particle swarm optimization (PSO) and Markov chain Monte Carlo sampling (MCMC), in which the PSO evolutionary algorithm and MCMC aim to find the target state and appearance template, respectively. The PSO can handle multi-modality in the target state and is therefore superior to a standard particle filter. Thus, PSO–MCMC achieves better performance in terms of accuracy when compared to the recently proposed particle MCMC. Experimental results demonstrate that the proposed tracker adaptively updates the target template and outperforms state-of-the-art tracking methods on a benchmark dataset. © 2019 Elsevier B.V. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Particle swarm optimization–Markov Chain Monte Carlo for accurate visual tracking with adaptive template update | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.asoc.2019.04.014 | - |
dc.identifier.bibliographicCitation | Applied Soft Computing Journal, v.97 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000603367700019 | - |
dc.identifier.scopusid | 2-s2.0-85065014042 | - |
dc.citation.title | Applied Soft Computing Journal | - |
dc.citation.volume | 97 | - |
dc.type.docType | Article | - |
dc.publisher.location | 네델란드 | - |
dc.subject.keywordAuthor | Adaptive template update | - |
dc.subject.keywordAuthor | Markov chain Monte Carlo | - |
dc.subject.keywordAuthor | Particle swarm optimization | - |
dc.subject.keywordAuthor | Visual tracking | - |
dc.subject.keywordPlus | Markov processes | - |
dc.subject.keywordPlus | Monte Carlo methods | - |
dc.subject.keywordPlus | Target tracking | - |
dc.subject.keywordPlus | Adaptive template | - |
dc.subject.keywordPlus | Benchmark datasets | - |
dc.subject.keywordPlus | Markov chain monte carlo samplings | - |
dc.subject.keywordPlus | Markov Chain Monte-Carlo | - |
dc.subject.keywordPlus | Particle filter | - |
dc.subject.keywordPlus | Sampling method | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Visual Tracking | - |
dc.subject.keywordPlus | Particle swarm optimization (PSO) | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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