A study on implementation of real-time intelligent video surveillance system based on embedded module

Abstract Conventional surveillance systems for preventing accidents and incidents do not identify 95% thereof after 22 min when one person monitors a plurality of closed circuit televisions (CCTV). To address this issue, while computer-based intelligent video surveillance systems have been studied t...

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Autores principales:	Jin Su Kim, Min-Gu Kim, Sung Bum Pan
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Intelligent video surveillance system Intruder detection Fire detection Loitering detection Fall detection Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/20401b4acf2843e18a66a99fd0ee89de
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spelling	oai:doaj.org-article:20401b4acf2843e18a66a99fd0ee89de2021-11-28T12:07:03ZA study on implementation of real-time intelligent video surveillance system based on embedded module10.1186/s13640-021-00576-01687-5281https://doaj.org/article/20401b4acf2843e18a66a99fd0ee89de2021-11-01T00:00:00Zhttps://doi.org/10.1186/s13640-021-00576-0https://doaj.org/toc/1687-5281Abstract Conventional surveillance systems for preventing accidents and incidents do not identify 95% thereof after 22 min when one person monitors a plurality of closed circuit televisions (CCTV). To address this issue, while computer-based intelligent video surveillance systems have been studied to notify users of abnormal situations when they happen, it is not commonly used in real environment because of weakness of personal information leaks and high power consumption. To address this issue, intelligent video surveillance systems based on small devices have been studied. This paper suggests implement an intelligent video surveillance system based on embedded modules for intruder detection based on information learning, fire detection based on color and motion information, and loitering and fall detection based on human body motion. Moreover, an algorithm and an embedded module optimization method are applied for real-time processing. The implemented algorithm showed performance of 88.51% for intruder detection, 92.63% for fire detection, 80% for loitering detection and 93.54% for fall detection. The result of comparison before and after optimization about the algorithm processing time showed 50.53% of decrease, implying potential real-time driving of the intelligent image monitoring system based on embedded modules.Jin Su KimMin-Gu KimSung Bum PanSpringerOpenarticleIntelligent video surveillance systemIntruder detectionFire detectionLoitering detectionFall detectionElectronicsTK7800-8360ENEURASIP Journal on Image and Video Processing, Vol 2021, Iss 1, Pp 1-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Intelligent video surveillance system Intruder detection Fire detection Loitering detection Fall detection Electronics TK7800-8360
spellingShingle	Intelligent video surveillance system Intruder detection Fire detection Loitering detection Fall detection Electronics TK7800-8360 Jin Su Kim Min-Gu Kim Sung Bum Pan A study on implementation of real-time intelligent video surveillance system based on embedded module
description	Abstract Conventional surveillance systems for preventing accidents and incidents do not identify 95% thereof after 22 min when one person monitors a plurality of closed circuit televisions (CCTV). To address this issue, while computer-based intelligent video surveillance systems have been studied to notify users of abnormal situations when they happen, it is not commonly used in real environment because of weakness of personal information leaks and high power consumption. To address this issue, intelligent video surveillance systems based on small devices have been studied. This paper suggests implement an intelligent video surveillance system based on embedded modules for intruder detection based on information learning, fire detection based on color and motion information, and loitering and fall detection based on human body motion. Moreover, an algorithm and an embedded module optimization method are applied for real-time processing. The implemented algorithm showed performance of 88.51% for intruder detection, 92.63% for fire detection, 80% for loitering detection and 93.54% for fall detection. The result of comparison before and after optimization about the algorithm processing time showed 50.53% of decrease, implying potential real-time driving of the intelligent image monitoring system based on embedded modules.
format	article
author	Jin Su Kim Min-Gu Kim Sung Bum Pan
author_facet	Jin Su Kim Min-Gu Kim Sung Bum Pan
author_sort	Jin Su Kim
title	A study on implementation of real-time intelligent video surveillance system based on embedded module
title_short	A study on implementation of real-time intelligent video surveillance system based on embedded module
title_full	A study on implementation of real-time intelligent video surveillance system based on embedded module
title_fullStr	A study on implementation of real-time intelligent video surveillance system based on embedded module
title_full_unstemmed	A study on implementation of real-time intelligent video surveillance system based on embedded module
title_sort	study on implementation of real-time intelligent video surveillance system based on embedded module
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/20401b4acf2843e18a66a99fd0ee89de
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A study on implementation of real-time intelligent video surveillance system based on embedded module

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