New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance

Human action recognition is an active research area in computer vision because of its immense application in the field of video surveillance, video retrieval, security systems, video indexing and human computer interaction. Action recognition is classified as the time varying feature data generated...

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Main Author:	Amir Sjarif, Nilam Nur
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/77777/1/NilamNurAmirPFC2015.pdf
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id	my-utm-ep.77777
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spelling	my-utm-ep.777772018-07-04T11:44:25Z New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance 2015-03 Amir Sjarif, Nilam Nur QA75 Electronic computers. Computer science Human action recognition is an active research area in computer vision because of its immense application in the field of video surveillance, video retrieval, security systems, video indexing and human computer interaction. Action recognition is classified as the time varying feature data generated by human under different viewpoint that aims to build mapping between dynamic image information and semantic understanding. Although a great deal of progress has been made in recognition of human actions during last two decades, few proposed approaches in literature are reported. This leads to a need for much research works to be conducted in addressing on going challenges leading to developing more efficient approaches to solve human action recognition. Feature extraction is the main tasks in action recognition that represents the core of any action recognition procedure. The process of feature extraction involves transforming the input data that describe the shape of a segmented silhouette of a moving person into the set of represented features of action poses. In video surveillance, global moment invariant based on Geometrical Moment Invariant (GMI) is widely used in human action recognition. However, there are many drawbacks of GMI such that it lack of granular interpretation of the invariants relative to the shape. Consequently, the representation of features has not been standardized. Hence, this study proposes a new scheme of human action recognition (HAR) with geometrical moment invariants for feature extraction and supervised invariant discretization in identifying actions uniqueness in video sequencing. The proposed scheme is tested using IXMAS dataset in video sequence that has non rigid nature of human poses that resulting from drastic illumination changes, changing in pose and erratic motion patterns. The invarianceness of the proposed scheme is validated based on the intra-class and inter-class analysis. The result of the proposed scheme yields better performance in action recognition compared to the conventional scheme with an average of more than 99% accuracy while preserving the shape of the human actions in video images. 2015-03 Thesis http://eprints.utm.my/id/eprint/77777/ http://eprints.utm.my/id/eprint/77777/1/NilamNurAmirPFC2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:96733 phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Amir Sjarif, Nilam Nur New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance
description	Human action recognition is an active research area in computer vision because of its immense application in the field of video surveillance, video retrieval, security systems, video indexing and human computer interaction. Action recognition is classified as the time varying feature data generated by human under different viewpoint that aims to build mapping between dynamic image information and semantic understanding. Although a great deal of progress has been made in recognition of human actions during last two decades, few proposed approaches in literature are reported. This leads to a need for much research works to be conducted in addressing on going challenges leading to developing more efficient approaches to solve human action recognition. Feature extraction is the main tasks in action recognition that represents the core of any action recognition procedure. The process of feature extraction involves transforming the input data that describe the shape of a segmented silhouette of a moving person into the set of represented features of action poses. In video surveillance, global moment invariant based on Geometrical Moment Invariant (GMI) is widely used in human action recognition. However, there are many drawbacks of GMI such that it lack of granular interpretation of the invariants relative to the shape. Consequently, the representation of features has not been standardized. Hence, this study proposes a new scheme of human action recognition (HAR) with geometrical moment invariants for feature extraction and supervised invariant discretization in identifying actions uniqueness in video sequencing. The proposed scheme is tested using IXMAS dataset in video sequence that has non rigid nature of human poses that resulting from drastic illumination changes, changing in pose and erratic motion patterns. The invarianceness of the proposed scheme is validated based on the intra-class and inter-class analysis. The result of the proposed scheme yields better performance in action recognition compared to the conventional scheme with an average of more than 99% accuracy while preserving the shape of the human actions in video images.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Amir Sjarif, Nilam Nur
author_facet	Amir Sjarif, Nilam Nur
author_sort	Amir Sjarif, Nilam Nur
title	New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance
title_short	New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance
title_full	New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance
title_fullStr	New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance
title_full_unstemmed	New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance
title_sort	new human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2015
url	http://eprints.utm.my/id/eprint/77777/1/NilamNurAmirPFC2015.pdf
_version_	1747817828424089600

New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance

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