Human activity recognition for video surveillance using sequences of postures

The Human activities recognition has become a research area of great interest as it has many potential applications; including automated surveillance, sign language interpretation and human-computer interfaces. In recent years, an extensive research has been conducted in this field. This paper...

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Main Authors:	Htike, Kyaw Kyaw, Khalifa, Othman Omran, Mohd. Ramli, Huda Adibah, Abushariah, Mohammad A. M.
Format:	Conference or Workshop Item
Language:	English English English
Published:	IEEE 2014
Subjects:	T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/1/123.pdf http://irep.iium.edu.my/38609/6/search.pdf http://irep.iium.edu.my/38609/12/38609_Human%20activity%20recognition%20for%20video.SCOPUSpdf.pdf

id	iium-38609
recordtype	eprints
spelling	iium-386092017-09-18T02:02:18Z http://irep.iium.edu.my/38609/ Human activity recognition for video surveillance using sequences of postures Htike, Kyaw Kyaw Khalifa, Othman Omran Mohd. Ramli, Huda Adibah Abushariah, Mohammad A. M. T10.5 Communication of technical information The Human activities recognition has become a research area of great interest as it has many potential applications; including automated surveillance, sign language interpretation and human-computer interfaces. In recent years, an extensive research has been conducted in this field. This paper presents a part of a novel a Human posture recognition system for video surveillance using one static camera. The training and testing stages were implemented using four different classifiers which are K Means, Fuzzy C Means, Multilayer Perceptron SelfOrganizing Maps and Feedforward Neural networks. The accuracy recognition of used classifiers is calculated. The results indicate that Self-Organizing Maps shows the highest recognition rate. Moreover, results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition. Furthermore, for each individual classifier, the recognition rate has been found to be proportional to the number of training postures. Performance comparisons between the proposed system and existing similar systems were also shown. IEEE 2014-04 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/38609/1/123.pdf application/pdf en http://irep.iium.edu.my/38609/6/search.pdf application/pdf en http://irep.iium.edu.my/38609/12/38609_Human%20activity%20recognition%20for%20video.SCOPUSpdf.pdf Htike, Kyaw Kyaw and Khalifa, Othman Omran and Mohd. Ramli, Huda Adibah and Abushariah, Mohammad A. M. (2014) Human activity recognition for video surveillance using sequences of postures. In: Third International Conference on e-Technologies and Networks for Development (ICeND2014), April 29 - May 1, 2014, Campus of Hadath, Beirut, Lebanon. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6991357 https://doi.org/10.1109/ICeND.2014.6991357
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	T10.5 Communication of technical information
spellingShingle	T10.5 Communication of technical information Htike, Kyaw Kyaw Khalifa, Othman Omran Mohd. Ramli, Huda Adibah Abushariah, Mohammad A. M. Human activity recognition for video surveillance using sequences of postures
description	The Human activities recognition has become a research area of great interest as it has many potential applications; including automated surveillance, sign language interpretation and human-computer interfaces. In recent years, an extensive research has been conducted in this field. This paper presents a part of a novel a Human posture recognition system for video surveillance using one static camera. The training and testing stages were implemented using four different classifiers which are K Means, Fuzzy C Means, Multilayer Perceptron SelfOrganizing Maps and Feedforward Neural networks. The accuracy recognition of used classifiers is calculated. The results indicate that Self-Organizing Maps shows the highest recognition rate. Moreover, results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition. Furthermore, for each individual classifier, the recognition rate has been found to be proportional to the number of training postures. Performance comparisons between the proposed system and existing similar systems were also shown.
format	Conference or Workshop Item
author	Htike, Kyaw Kyaw Khalifa, Othman Omran Mohd. Ramli, Huda Adibah Abushariah, Mohammad A. M.
author_facet	Htike, Kyaw Kyaw Khalifa, Othman Omran Mohd. Ramli, Huda Adibah Abushariah, Mohammad A. M.
author_sort	Htike, Kyaw Kyaw
title	Human activity recognition for video surveillance using sequences of postures
title_short	Human activity recognition for video surveillance using sequences of postures
title_full	Human activity recognition for video surveillance using sequences of postures
title_fullStr	Human activity recognition for video surveillance using sequences of postures
title_full_unstemmed	Human activity recognition for video surveillance using sequences of postures
title_sort	human activity recognition for video surveillance using sequences of postures
publisher	IEEE
publishDate	2014
url	http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/ http://irep.iium.edu.my/38609/1/123.pdf http://irep.iium.edu.my/38609/6/search.pdf http://irep.iium.edu.my/38609/12/38609_Human%20activity%20recognition%20for%20video.SCOPUSpdf.pdf
first_indexed	2023-09-18T20:55:28Z
last_indexed	2023-09-18T20:55:28Z
_version_	1777410274278506496

Human activity recognition for video surveillance using sequences of postures

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