Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance....

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Autores principales:	Alem Fitwi, Yu Chen, Han Sun, Robert Harrod
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	area estimation crowd management COVID-19 edge camera interpersonal distance social distancing Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/733671c87e3f4711ac28cf5ade1a5967
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spelling	oai:doaj.org-article:733671c87e3f4711ac28cf5ade1a59672021-11-25T17:17:25ZEstimating Interpersonal Distance and Crowd Density with a Single-Edge Camera10.3390/computers101101432073-431Xhttps://doaj.org/article/733671c87e3f4711ac28cf5ade1a59672021-11-01T00:00:00Zhttps://www.mdpi.com/2073-431X/10/11/143https://doaj.org/toc/2073-431XFor public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.Alem FitwiYu ChenHan SunRobert HarrodMDPI AGarticlearea estimationcrowd managementCOVID-19edge camerainterpersonal distancesocial distancingElectronic computers. Computer scienceQA75.5-76.95ENComputers, Vol 10, Iss 143, p 143 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	area estimation crowd management COVID-19 edge camera interpersonal distance social distancing Electronic computers. Computer science QA75.5-76.95
spellingShingle	area estimation crowd management COVID-19 edge camera interpersonal distance social distancing Electronic computers. Computer science QA75.5-76.95 Alem Fitwi Yu Chen Han Sun Robert Harrod Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera
description	For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.
format	article
author	Alem Fitwi Yu Chen Han Sun Robert Harrod
author_facet	Alem Fitwi Yu Chen Han Sun Robert Harrod
author_sort	Alem Fitwi
title	Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera
title_short	Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera
title_full	Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera
title_fullStr	Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera
title_full_unstemmed	Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera
title_sort	estimating interpersonal distance and crowd density with a single-edge camera
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/733671c87e3f4711ac28cf5ade1a5967
work_keys_str_mv	AT alemfitwi estimatinginterpersonaldistanceandcrowddensitywithasingleedgecamera AT yuchen estimatinginterpersonaldistanceandcrowddensitywithasingleedgecamera AT hansun estimatinginterpersonaldistanceandcrowddensitywithasingleedgecamera AT robertharrod estimatinginterpersonaldistanceandcrowddensitywithasingleedgecamera
_version_	1718412537821659136

Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera

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