Open AccessCovid-19 Monitoring System Using Social Distancing Face Mask Detection and Classification on Live Video
Author(s) -
Prof. Simran Pal R,
Aaliya Syedi,
Aditya C Kumar,
D. Amrutha,
Akriti Raj
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41101
Subject(s) - computer science , artificial intelligence , dbscan , face detection , cluster analysis , convolutional neural network , computer vision , pattern recognition (psychology) , classifier (uml) , facial recognition system , object class detection , fuzzy clustering , canopy clustering algorithm
The spread of the Coronavirus has prompted individuals to remain indoors and adhere to COVID- appropriate practices, which include social distance, the use of face masks, hand sanitizers, and other measures to protectthemselves against infection. In heavily populated locations with limited resources, it is impossible to physically supervise compliance with these standards. As a result, an automated, lightweight, and powerful video monitoring system is required to make the process more efficient. This paper proposes an extensive and productive solution for performing person detection, social distance detection, face mask detection, and face mask classification using object detection, clustering, and Convolution Neural Networks (CNN) On video datasets, in addition to YOLOv3, density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD), and MobileNetV2 based binary classifier other techniques have also been used to achieve the predicted outcomes. This study also provides parallels for numerous face mask detection and classification models. Keywords: COVID-19, DBSCAN, DSFD, YOLOv3, MobileNetV2, CNN, clustering, Social Distance, Face Masks