Perimeter intrusion prediction method using trajectory frequency and Naive Bayes classifier

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dc.contributor.authorYun, Joongsup
dc.contributor.authorShin, Hyosang
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2024-02-19T13:33:21Z
dc.date.available2024-02-19T13:33:21Z
dc.date.issued2023-11-20
dc.description.abstractThis paper proposes a novel perimeter intrusion prediction algorithm that can be applied to generic perimeter security systems. The proposed algorithm uses multiple probability mass functions that are computed using trajectory frequency information for different behaviour models: non-intrusive intention and intrusive intention. A Naive Bayes classifier is used to compute the intention probability for integrating multiple probabilities from different probability mass functions. The performance of the proposed algorithm is validated by numerical simulations and the classification characteristics are also discussed.en_UK
dc.description.sponsorshipInnovate UK and Operational Solutions Ltd. [grant number KTP12998].en_UK
dc.identifier.citationYun J, Shin HS, Tsourdos A., (2023) Perimeter intrusion prediction method using trajectory frequency and Naive Bayes classifier. In 2023 23rd International Conference on Control, Automation and Systems (ICCAS), 17-20 October 2023, Yeosu, Korea, pp. 1610-1615en_UK
dc.identifier.eisbn978-89-93215-27-4
dc.identifier.eissn2642-3901
dc.identifier.isbn979-8-3503-9454-2
dc.identifier.issn1598-7833
dc.identifier.urihttps://doi.org/10.23919/ICCAS59377.2023.10316828
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20826
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectperimeter intrusion detection systemsen_UK
dc.subjecttarget classificationen_UK
dc.subjecttrajectory frequencyen_UK
dc.subjectnaive Bayesen_UK
dc.titlePerimeter intrusion prediction method using trajectory frequency and Naive Bayes classifieren_UK
dc.typeConference paperen_UK
dcterms.dateAccepted2023-07-31

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