COVID-19 Social Distancing Detection and Email Violation Mechanisms

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dc.contributor.authorBhojak, D.
dc.contributor.authorJat, T.
dc.contributor.authorNaik, D.
dc.date.accessioned2026-02-06T06:34:55Z
dc.date.issued2023
dc.description.abstractThe technique of flattening the curve for coronavirus-infected cases is challenging in addressing the worldwide ongoing rampant novel COVID-19 pandemic crisis unless citizens take steps to halt the virus’s spread. Maintaining a safe space between individuals around us in public is one of the most important behaviors. Deep learning algorithms have been used in the proposed work to mitigate the spreading of the coronavirus utilizing social distance detection. This proposed work analyzes a pre-recorded video feed of walking pedestrians to alert people to maintain a safe distance. The goal is achieved using YOLOv3 and YOLOv4 for object detection in the video frame used as input. Furthermore, an email-based alert mechanism is also implemented if the number of violations exceeds the defined limit. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
dc.identifier.citationLecture Notes in Electrical Engineering, 2023, Vol.997 LNEE, , p. 493-502
dc.identifier.issn18761100
dc.identifier.urihttps://doi.org/10.1007/978-981-99-0085-5_40
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29519
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.subjectAlert mechanisms
dc.subjectCOVID-19
dc.subjectDeep learning
dc.subjectDistance estimation
dc.subjectPedestrian detection and tracking
dc.subjectSocial distancing
dc.subjectYOLOv3
dc.subjectYOLOv4
dc.titleCOVID-19 Social Distancing Detection and Email Violation Mechanisms

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