Computational Intelligence Based Point of Interest Detection by Video Surveillance Implementations

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dc.contributor.authorTercan, Emre
dc.contributor.authorTapkin, Serkan
dc.contributor.authorKucuk, Furkan
dc.contributor.authorDemirtas, Ali
dc.contributor.authorOzbayoglu, Ahmet
dc.contributor.authorTurker, Abdussamet
dc.date.accessioned2024-10-04T18:49:24Z
dc.date.available2024-10-04T18:49:24Z
dc.date.issued2023
dc.departmentBayburt Üniversitesien_US
dc.description.abstractLatest advancement of the computer vision literature and Convolutional Neural Networks (CNN) reveal many opportunities that are being actively used in various research areas. One of the most important examples for these areas is autonomous vehicles and mapping systems. Point of interest detection is a rising field within autonomous video tracking and autonomous mapping systems. Within the last few years, the number of implementations and research papers started rising due to the advancements in the new deep learning systems. In this paper, our aim is to survey the existing studies implemented on point of interest detection systems that focus on objects on the road (like lanes, road marks), or objects on the roadside (like road signs, restaurants or temporary establishments) so that they can be used for autonomous vehicles and automatic mapping systems. Meanwhile, the roadside point of interest detection problem has been addressed from a transportation industry perspective. At the same time, a deep learning based point of interest detection model based on roadside gas station identification will be introduced as proof of the anticipated concept. Instead of using an internet connection for point of interest retrieval, the proposed model has the capability to work offline for more robustness. A variety of models have been analysed and their detection speed and accuracy performances are compared. Our preliminary results show that it is possible to develop a model achieving a satisfactory real-time performance that can be embedded into autonomous cars such that streaming video analysis and point of interest detection might be achievable in actual utilisation for future implementations.en_US
dc.identifier.doi10.34028/iajit/20/6/7
dc.identifier.endpage910en_US
dc.identifier.issn1683-3198
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85176142923en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage899en_US
dc.identifier.urihttps://doi.org/10.34028/iajit/20/6/7
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3115
dc.identifier.volume20en_US
dc.identifier.wosWOS:001180197300007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherZarka Private Univen_US
dc.relation.ispartofInternational Arab Journal of Information Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPoint of interest detectionen_US
dc.subjectYOLO algorithmen_US
dc.subjectR-CNNen_US
dc.subjectTOODen_US
dc.subjectdeep learning.en_US
dc.titleComputational Intelligence Based Point of Interest Detection by Video Surveillance Implementationsen_US
dc.typeArticleen_US

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