Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim

Unlawful behavior detection is one of the important research topic in Video Surveillance System (VSS). This is usually done manually by human. However, this is unfeasible due to the size of images that need to be scan through. Moreover, human are prone to misjudgment. Behaviors are usually detected...

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Main Author:	Mohamed Hatim, Shahirah
Format:	Thesis
Language:	English
Published:	2016
Online Access:	http://ir.uitm.edu.my/id/eprint/18623/ http://ir.uitm.edu.my/id/eprint/18623/1/TM_SHAHIRAH%20MOHAMED%20HATIM%20CS%2016_5.pdf

id	uitm-18623
recordtype	eprints
spelling	uitm-186232018-01-14T08:16:41Z http://ir.uitm.edu.my/id/eprint/18623/ Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim Mohamed Hatim, Shahirah Unlawful behavior detection is one of the important research topic in Video Surveillance System (VSS). This is usually done manually by human. However, this is unfeasible due to the size of images that need to be scan through. Moreover, human are prone to misjudgment. Behaviors are usually detected through surveillance camera in the form of video recording. Video scenes are sequence of picture frame. The focus of this research is to identify and detect unlawful behavior in an academic restricted area. A total number of 95 videos used in the research are based on different types of hand movement which are knocking, twisting, waving and clapping. The videos are stored in avi format which are sampled to the resolution of 200x164 pixels. Each video is of less than 30 seconds length. The data undergo the pre-processed phase which consists of edge detection, adaptive thresholding segmentation and MATLAB regionprops function for feature extraction. The main goal of the research is to apply the concept of Genetic Algorithm (GA) that can classify hand movements as unlawful behavior in videos. GA is used as the method of unlawful behavior detection. Previous research on GA components impact evaluation has identified selection parameter as high potential of increasing GA performance for unlawful behavior detection. Two types of selection parameter namely tournament selection (TOS) and random permutation selection (RPS) are chosen. From the result and analysis obtained in this research, it is established that both TOS and RPS are comparable in terms of the detection rate, specificity, false positive rate, false negative rate and accuracy. It is proven that TOS gives better result of detection than RPS. 2016 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/18623/1/TM_SHAHIRAH%20MOHAMED%20HATIM%20CS%2016_5.pdf Mohamed Hatim, Shahirah (2016) Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim. Masters thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
description	Unlawful behavior detection is one of the important research topic in Video Surveillance System (VSS). This is usually done manually by human. However, this is unfeasible due to the size of images that need to be scan through. Moreover, human are prone to misjudgment. Behaviors are usually detected through surveillance camera in the form of video recording. Video scenes are sequence of picture frame. The focus of this research is to identify and detect unlawful behavior in an academic restricted area. A total number of 95 videos used in the research are based on different types of hand movement which are knocking, twisting, waving and clapping. The videos are stored in avi format which are sampled to the resolution of 200x164 pixels. Each video is of less than 30 seconds length. The data undergo the pre-processed phase which consists of edge detection, adaptive thresholding segmentation and MATLAB regionprops function for feature extraction. The main goal of the research is to apply the concept of Genetic Algorithm (GA) that can classify hand movements as unlawful behavior in videos. GA is used as the method of unlawful behavior detection. Previous research on GA components impact evaluation has identified selection parameter as high potential of increasing GA performance for unlawful behavior detection. Two types of selection parameter namely tournament selection (TOS) and random permutation selection (RPS) are chosen. From the result and analysis obtained in this research, it is established that both TOS and RPS are comparable in terms of the detection rate, specificity, false positive rate, false negative rate and accuracy. It is proven that TOS gives better result of detection than RPS.
format	Thesis
author	Mohamed Hatim, Shahirah
spellingShingle	Mohamed Hatim, Shahirah Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim
author_facet	Mohamed Hatim, Shahirah
author_sort	Mohamed Hatim, Shahirah
title	Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim
title_short	Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim
title_full	Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim
title_fullStr	Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim
title_full_unstemmed	Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim
title_sort	identifying and detecting unlawful behavior in video images using genetic algorithm / shahirah mohamed hatim
publishDate	2016
url	http://ir.uitm.edu.my/id/eprint/18623/ http://ir.uitm.edu.my/id/eprint/18623/1/TM_SHAHIRAH%20MOHAMED%20HATIM%20CS%2016_5.pdf
first_indexed	2023-09-18T23:00:57Z
last_indexed	2023-09-18T23:00:57Z
_version_	1777418168929615872

Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim

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