Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/5552
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dc.contributor.authorDas, Sunanda-
dc.contributor.authorR, Chinnaiyan-
dc.contributor.authorG, Sabarmathi-
dc.contributor.authorA, Maskey-
dc.contributor.authorM, Swarnamugi-
dc.contributor.authorS, Balachandar-
dc.contributor.authorR, Divya-
dc.date.accessioned2024-02-01T04:07:14Z-
dc.date.available2024-02-01T04:07:14Z-
dc.date.issued2023-
dc.identifier.citationVol. 371 ;pp. 567577en_US
dc.identifier.isbn9789819967056-
dc.identifier.urihttps://doi.org/10.1007/9789819967063_50-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/5552-
dc.description.abstractThe World Health Organization (WHO) suggests social isolation as a remedy to lessen the transmission of COVID19 in public areas. Most countries and national health authorities have established the 2m physical distance as a required safety measure in shopping malls, schools, and other covered locations. In this study, we use standard CCTV security cameras to create an automated system for people detecting crowds in indoor and outdoor settings. Popular computer vision algorithms and the CNN model are implemented to build up the system and a comparative study is performed with algorithms like Support Vector Machine and KNN algorithm. The created model is a general and precise people tracking and identifying the solution that may be used in a wide range of other study areas where the focus is on person detection, including autonomous cars, anomaly detection, crowd analysis, and many more. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.language.isoenen_US
dc.publisherSmart Innovation, Systems and Technologiesen_US
dc.subjectCrowd Monitoring And Analysisen_US
dc.subjectFace Landmark Estimationen_US
dc.subjectHistogram Of Gradient (Hog)en_US
dc.subjectKNearest Neighbor (Knn)en_US
dc.subjectSupport Vector Machine (Svm)en_US
dc.titleCrowd Monitoring System Using Facial Recognitionen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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