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...
| Main Authors: | , , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
IEEE
2017
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| Subjects: | |
| Online Access: | 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 |
| _version_ | 1848785881035440128 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T17:00:11Z |
| format | Proceeding Paper |
| id | iium-62659 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:00:11Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 Proceeding Paper 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 |
| 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 |
| title | 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_short | Automated daily human activity recognition for video surveillance using neural network |
| title_sort | automated daily human activity recognition for video surveillance using neural network |
| topic | T Technology (General) |
| 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 |