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...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English English |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | 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 |
| _version_ | 1848781636689199104 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T15:52:43Z |
| format | Proceeding Paper |
| id | iium-38609 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T15:52:43Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 Proceeding Paper 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 |
| 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 |
| title | 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_short | Human activity recognition for video surveillance using sequences of postures |
| title_sort | human activity recognition for video surveillance using sequences of postures |
| topic | T10.5 Communication of technical information |
| 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 |