Artificial Intelligence Based Social Distance Monitoring in Public Areas

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dc.contributor.authorAlbayrak, Abdulkadir
dc.date.accessioned2024-04-24T17:18:18Z
dc.date.available2024-04-24T17:18:18Z
dc.date.issued2022
dc.departmentDicle Üniversitesien_US
dc.description.abstractCOVID-19 is an infectious disease caused by a newly discovered coronavirus called SARS-CoV-2. There are two ways of contamination risk, namely spreading through droplets or aerosol-type spreading into the air with people's speech in crowded environments. The best way to prevent the spread of COVID-19 in a crowd public area is to follow social distance rules. Violation of the social distance is a common situation in areas where people frequently visit such as hospitals, schools and shopping centers. In this study, an artificial intelligence -based social distance determination study was developed in order to detect social distance violations in crowded areas. Within the scope of the study, a new dataset was proposed to determine social distance between pedestrians. The YOLOv3 algorithm, which is very successful in object detection, was compared with the SSD-MobileNET, which is considered to be a light weighted model, and the traditionally handcrafted methods Haar-like cascade and HOG methods. Inability to obtain depth information, which is one of the biggest problems encountered in monocular cameras, has been tried to be eliminated by perspective transformation. In this way, the social distance violation detected in specific area is notified by the system to the relevant people with a warning.en_US
dc.identifier.doi10.18280/ts.390323
dc.identifier.endpage967en_US
dc.identifier.issn0765-0019
dc.identifier.issn1958-5608
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85135592111
dc.identifier.scopusqualityQ3
dc.identifier.startpage961en_US
dc.identifier.urihttps://doi.org/10.18280/ts.390323
dc.identifier.urihttps://hdl.handle.net/11468/18720
dc.identifier.volume39en_US
dc.identifier.wosWOS:000834793500023
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInt Information & Engineering Technology Assocen_US
dc.relation.ispartofTraitement Du Signal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSocial Distancing Pedestrian Detectionen_US
dc.subjectPedestrian Detectionen_US
dc.subjectCovid-19en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDeep Learningen_US
dc.titleArtificial Intelligence Based Social Distance Monitoring in Public Areasen_US
dc.titleArtificial Intelligence Based Social Distance Monitoring in Public Areas
dc.typeArticleen_US

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