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Published in:

Volume 8 Issue 1
January-2021
eISSN: 2349-5162

Unique Identifier

JETIR2101004

Page Number

21-46

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Title

Social Distance Detection

ISSN

2349-5162

Cite This Article

"Social Distance Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 1, page no.21-46, January-2021, Available :http://www.jetir.org/papers/JETIR2101004.pdf

Abstract

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-m physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras.

Key Words

social distancing; COVID-19; human detection and tracking; distance estimation; deep convolutional neural networks; crowd monitoring; pedestrian detection; inverse perspective mapping

Cite This Article

"Social Distance Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 1, page no. pp21-46, January-2021, Available at : http://www.jetir.org/papers/JETIR2101004.pdf

Publication Details

Published Paper ID: JETIR2101004
Registration ID: 304767
Published In: Volume 8 | Issue 1 | Year January-2021
DOI (Digital Object Identifier):
Page No: 21-46
ISSN Number: 2349-5162

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Cite This Article

"Social Distance Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 1, page no. pp21-46, January-2021, Available at : http://www.jetir.org/papers/JETIR2101004.pdf




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