Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants

Installation of surveillance cameras in nuclear power plants is critical to protecting the facilities against terrorist attacks or monitoring the reactor operator. This has led to large amounts of video surveillance data, creating a demand for automatic detection of anomalies or suspicious movements...

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Autores principales:	Daisuke MIKI, Shinya ABE, Shi CHEN, Kazuyuki DEMACHI
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2020
Materias:	nuclear security video surveillance human motion recognition convolutional neural network image processing Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/7633f93111e14a0e853e56e460f61497
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spelling	oai:doaj.org-article:7633f93111e14a0e853e56e460f614972021-11-29T05:56:30ZRobust human motion recognition from wide-angle images for video surveillance in nuclear power plants2187-974510.1299/mej.19-00533https://doaj.org/article/7633f93111e14a0e853e56e460f614972020-03-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/7/3/7_19-00533/_pdf/-char/enhttps://doaj.org/toc/2187-9745Installation of surveillance cameras in nuclear power plants is critical to protecting the facilities against terrorist attacks or monitoring the reactor operator. This has led to large amounts of video surveillance data, creating a demand for automatic detection of anomalies or suspicious movements. Tracking human motion from video sequences is a notable technique used for detecting anomalies in human behavior and is currently achieved with the use of a depth camera. However, depth cameras require a complicated camera system and their field of view is limited. To overcome this problem, there is a need for recognizing human motion in wide-angle images – a view that often causes distortion. In this study, we devised a method for tracking human motion through wideangle image distortion. The main contribution of this study is a methodology that automatically estimates the transformation parameters needed to improve the accuracy of motion recognition; these parameters are applied to a distorted wide-angle image in every frame. We propose a new multi-layered convolutional neural architecture for estimating the locations of human joints in images and transformation parameters simultaneously. When applied to distorted wide-angle images, the robustness of our method is demonstrated through a quantitative evaluation of human joint location prediction. In addition, we compare our method with a motion tracking system and an infrared-camera-based motion capture system to demonstrate its ability to handle wide-angle and close-range images.Daisuke MIKIShinya ABEShi CHENKazuyuki DEMACHIThe Japan Society of Mechanical Engineersarticlenuclear securityvideo surveillancehuman motion recognitionconvolutional neural networkimage processingMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 7, Iss 3, Pp 19-00533-19-00533 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	nuclear security video surveillance human motion recognition convolutional neural network image processing Mechanical engineering and machinery TJ1-1570
spellingShingle	nuclear security video surveillance human motion recognition convolutional neural network image processing Mechanical engineering and machinery TJ1-1570 Daisuke MIKI Shinya ABE Shi CHEN Kazuyuki DEMACHI Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants
description	Installation of surveillance cameras in nuclear power plants is critical to protecting the facilities against terrorist attacks or monitoring the reactor operator. This has led to large amounts of video surveillance data, creating a demand for automatic detection of anomalies or suspicious movements. Tracking human motion from video sequences is a notable technique used for detecting anomalies in human behavior and is currently achieved with the use of a depth camera. However, depth cameras require a complicated camera system and their field of view is limited. To overcome this problem, there is a need for recognizing human motion in wide-angle images – a view that often causes distortion. In this study, we devised a method for tracking human motion through wideangle image distortion. The main contribution of this study is a methodology that automatically estimates the transformation parameters needed to improve the accuracy of motion recognition; these parameters are applied to a distorted wide-angle image in every frame. We propose a new multi-layered convolutional neural architecture for estimating the locations of human joints in images and transformation parameters simultaneously. When applied to distorted wide-angle images, the robustness of our method is demonstrated through a quantitative evaluation of human joint location prediction. In addition, we compare our method with a motion tracking system and an infrared-camera-based motion capture system to demonstrate its ability to handle wide-angle and close-range images.
format	article
author	Daisuke MIKI Shinya ABE Shi CHEN Kazuyuki DEMACHI
author_facet	Daisuke MIKI Shinya ABE Shi CHEN Kazuyuki DEMACHI
author_sort	Daisuke MIKI
title	Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants
title_short	Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants
title_full	Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants
title_fullStr	Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants
title_full_unstemmed	Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants
title_sort	robust human motion recognition from wide-angle images for video surveillance in nuclear power plants
publisher	The Japan Society of Mechanical Engineers
publishDate	2020
url	https://doaj.org/article/7633f93111e14a0e853e56e460f61497
work_keys_str_mv	AT daisukemiki robusthumanmotionrecognitionfromwideangleimagesforvideosurveillanceinnuclearpowerplants AT shinyaabe robusthumanmotionrecognitionfromwideangleimagesforvideosurveillanceinnuclearpowerplants AT shichen robusthumanmotionrecognitionfromwideangleimagesforvideosurveillanceinnuclearpowerplants AT kazuyukidemachi robusthumanmotionrecognitionfromwideangleimagesforvideosurveillanceinnuclearpowerplants
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Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants

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