Automated daily human activity recognition for video surveillance using neural network

Surveillance video systems are gaining increasing attention in the field of computer vision due to its demands of users for the seek of security. It is promising to observe the human movement and predict such kind of sense of movements. The need arises to develop a surveillance system that capable t...

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Main Authors:	Babiker, Mohanad, Khalifa, Othman Omran, Htike, Kyaw Kyaw, Hassan Abdalla Hashim, Aisha, Zaharadeen, Muhamed
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2017
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/62659/ http://irep.iium.edu.my/62659/ http://irep.iium.edu.my/62659/ http://irep.iium.edu.my/62659/1/62659_Automated%20daily%20human%20activity%20recognition.pdf http://irep.iium.edu.my/62659/7/62659_Automated%20daily%20human%20activity%20recognition%20for%20video%20surveillance_SCOPUS%20Conf.pdf

id	iium-62659
recordtype	eprints
spelling	iium-626592018-09-06T09:23:02Z http://irep.iium.edu.my/62659/ Automated daily human activity recognition for video surveillance using neural network Babiker, Mohanad Khalifa, Othman Omran Htike, Kyaw Kyaw Hassan Abdalla Hashim, Aisha Zaharadeen, Muhamed T Technology (General) Surveillance video systems are gaining increasing attention in the field of computer vision due to its demands of users for the seek of security. It is promising to observe the human movement and predict such kind of sense of movements. The need arises to develop a surveillance system that capable to overcome the shortcoming of depending on the human resource to stay monitoring, observing the normal and suspect event all the time without any absent mind and to facilitate the control of huge surveillance system network. In this paper, an intelligent human activity system recognition is developed. Series of digital image processing techniques were used in each stage of the proposed system, such as background subtraction, binarization, and morphological operation. A robust neural network was built based on the human activities features database, which was extracted from the frame sequences. Multi-layer feed forward perceptron network used to classify the activities model in the dataset. The classification results show a high performance in all of the stages of training, testing and validation. Finally, these results lead to achieving a promising performance in the activity recognition rate. IEEE 2017-11-28 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/62659/1/62659_Automated%20daily%20human%20activity%20recognition.pdf application/pdf en http://irep.iium.edu.my/62659/7/62659_Automated%20daily%20human%20activity%20recognition%20for%20video%20surveillance_SCOPUS%20Conf.pdf Babiker, Mohanad and Khalifa, Othman Omran and Htike, Kyaw Kyaw and Hassan Abdalla Hashim, Aisha and Zaharadeen, Muhamed (2017) Automated daily human activity recognition for video surveillance using neural network. In: 4th IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA) 2017, 28th-30th November 2017, Putrajaya, Malaysia. http://doi.org/10.1109/ICSIMA.2017.8312024 10.1109/ICSIMA.2017.8312024
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Babiker, Mohanad Khalifa, Othman Omran Htike, Kyaw Kyaw Hassan Abdalla Hashim, Aisha Zaharadeen, Muhamed Automated daily human activity recognition for video surveillance using neural network
description	Surveillance video systems are gaining increasing attention in the field of computer vision due to its demands of users for the seek of security. It is promising to observe the human movement and predict such kind of sense of movements. The need arises to develop a surveillance system that capable to overcome the shortcoming of depending on the human resource to stay monitoring, observing the normal and suspect event all the time without any absent mind and to facilitate the control of huge surveillance system network. In this paper, an intelligent human activity system recognition is developed. Series of digital image processing techniques were used in each stage of the proposed system, such as background subtraction, binarization, and morphological operation. A robust neural network was built based on the human activities features database, which was extracted from the frame sequences. Multi-layer feed forward perceptron network used to classify the activities model in the dataset. The classification results show a high performance in all of the stages of training, testing and validation. Finally, these results lead to achieving a promising performance in the activity recognition rate.
format	Conference or Workshop Item
author	Babiker, Mohanad Khalifa, Othman Omran Htike, Kyaw Kyaw Hassan Abdalla Hashim, Aisha Zaharadeen, Muhamed
author_facet	Babiker, Mohanad Khalifa, Othman Omran Htike, Kyaw Kyaw Hassan Abdalla Hashim, Aisha Zaharadeen, Muhamed
author_sort	Babiker, Mohanad
title	Automated daily human activity recognition for video surveillance using neural network
title_short	Automated daily human activity recognition for video surveillance using neural network
title_full	Automated daily human activity recognition for video surveillance using neural network
title_fullStr	Automated daily human activity recognition for video surveillance using neural network
title_full_unstemmed	Automated daily human activity recognition for video surveillance using neural network
title_sort	automated daily human activity recognition for video surveillance using neural network
publisher	IEEE
publishDate	2017
url	http://irep.iium.edu.my/62659/ http://irep.iium.edu.my/62659/ http://irep.iium.edu.my/62659/ http://irep.iium.edu.my/62659/1/62659_Automated%20daily%20human%20activity%20recognition.pdf http://irep.iium.edu.my/62659/7/62659_Automated%20daily%20human%20activity%20recognition%20for%20video%20surveillance_SCOPUS%20Conf.pdf
first_indexed	2023-09-18T21:28:46Z
last_indexed	2023-09-18T21:28:46Z
_version_	1777412370076794880

Automated daily human activity recognition for video surveillance using neural network

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