Unusual Activities Classification In Video Using Deep Learning Approach

Intelligent visual surveillance has received an increasing research attention due to the need for greater security in public areas like airports, shopping malls, railway stations, and crowded parks. According to a survey by the British Security Industry Association, more than five million CCTV camer...

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Main Author:	Tay, Nian Chi
Format:	Thesis
Published:	2020
Subjects:	QA75-76.95 Calculating machines
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id	my-mmu-ep.7743
record_format	uketd_dc
spelling	my-mmu-ep.77432020-09-21T07:23:48Z Unusual Activities Classification In Video Using Deep Learning Approach 2020-01 Tay, Nian Chi QA75-76.95 Calculating machines Intelligent visual surveillance has received an increasing research attention due to the need for greater security in public areas like airports, shopping malls, railway stations, and crowded parks. According to a survey by the British Security Industry Association, more than five million CCTV cameras have been installed around main cities in the United Kingdom. The widespread use of CCTV cameras leads to huge amount of data that need to be analysed for safety purpose. Analysis of these video footages is limited through manual inspection. This study proposes to perform intelligent detection of abnormal behaviour in video scenes. Efforts are made to design a system that can learn what a normal behaviour is, and is able to distinguish between a normal and an abnormal behaviour in a given context. Sometimes, a normal behaviour in a context may be abnormal in the others. For example, a running action is normal in a sports event but is considered abnormal in a shopping mall. The specific focuses of this research are (1) Two-person interactions, e.g. fighting, and (2) crowdbased interactions, e.g. people running in a crowd. The proposed study is useful for effective public monitoring and early prevention of incidents like accidents, crime and terrorism. The research also contributes towards new designed solutions of robust visual surveillance system for public security in smart cities. 2020-01 Thesis http://shdl.mmu.edu.my/7743/ http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Information Science & Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA75-76.95 Calculating machines
spellingShingle	QA75-76.95 Calculating machines Tay, Nian Chi Unusual Activities Classification In Video Using Deep Learning Approach
description	Intelligent visual surveillance has received an increasing research attention due to the need for greater security in public areas like airports, shopping malls, railway stations, and crowded parks. According to a survey by the British Security Industry Association, more than five million CCTV cameras have been installed around main cities in the United Kingdom. The widespread use of CCTV cameras leads to huge amount of data that need to be analysed for safety purpose. Analysis of these video footages is limited through manual inspection. This study proposes to perform intelligent detection of abnormal behaviour in video scenes. Efforts are made to design a system that can learn what a normal behaviour is, and is able to distinguish between a normal and an abnormal behaviour in a given context. Sometimes, a normal behaviour in a context may be abnormal in the others. For example, a running action is normal in a sports event but is considered abnormal in a shopping mall. The specific focuses of this research are (1) Two-person interactions, e.g. fighting, and (2) crowdbased interactions, e.g. people running in a crowd. The proposed study is useful for effective public monitoring and early prevention of incidents like accidents, crime and terrorism. The research also contributes towards new designed solutions of robust visual surveillance system for public security in smart cities.
format	Thesis
qualification_level	Master's degree
author	Tay, Nian Chi
author_facet	Tay, Nian Chi
author_sort	Tay, Nian Chi
title	Unusual Activities Classification In Video Using Deep Learning Approach
title_short	Unusual Activities Classification In Video Using Deep Learning Approach
title_full	Unusual Activities Classification In Video Using Deep Learning Approach
title_fullStr	Unusual Activities Classification In Video Using Deep Learning Approach
title_full_unstemmed	Unusual Activities Classification In Video Using Deep Learning Approach
title_sort	unusual activities classification in video using deep learning approach
granting_institution	Multimedia University
granting_department	Faculty of Information Science & Technology
publishDate	2020
_version_	1747829671853031424

Unusual Activities Classification In Video Using Deep Learning Approach

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