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dc.contributor.authorNenavath, Hathiram
dc.contributor.authorAshwini, K
dc.contributor.authorJatoth, Ravi Kumar
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2021-10-15T05:42:45Z
dc.date.available2021-10-15T05:42:45Z
dc.date.issued2022
dc.identifier.issn0019-0578
dc.identifier.doi10.1016/j.isatra.2021.09.014
dc.identifier.urihttp://hdl.handle.net/10072/409104
dc.description.abstractVisual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherElsevier BV
dc.relation.ispartofpagefrom460
dc.relation.ispartofpageto476
dc.relation.ispartofissuePart A
dc.relation.ispartofjournalISA Transactions
dc.relation.ispartofvolume128
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchManufacturing engineering
dc.subject.fieldofresearchElectronics, sensors and digital hardware
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode4008
dc.subject.fieldofresearchcode4014
dc.subject.fieldofresearchcode4009
dc.subject.keywordsOcclusion
dc.subject.keywordsParticle filter (PF)
dc.subject.keywordsSine Cosine Algorithm (SCA)
dc.subject.keywordsTrigonometric Particle Filter (TPF)
dc.subject.keywordsVisual tracking
dc.titleIntelligent Trigonometric Particle Filter for visual tracking
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationNenavath, H; Ashwini, K; Jatoth, RK; Mirjalili, S, Intelligent Trigonometric Particle Filter for visual tracking, ISA Transactions, 2022, 128 (Part A), pp. 460-476
dcterms.dateAccepted2021-09-21
dc.date.updated2021-10-14T23:12:31Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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