Intelligent human posture recognition in video sequences
Human posture recognition is gaining increasing attention in the field of computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computerinteraction and surveillance systems. Human posture recognition in video sequences is a challengi...
| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/23110/ http://irep.iium.edu.my/23110/1/p90.pdf |
| _version_ | 1848779106711240704 |
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| author | Khalifa, Othman Omran Htike Ali, Kyaw Kyaw Lai, Weng Kai |
| author_facet | Khalifa, Othman Omran Htike Ali, Kyaw Kyaw Lai, Weng Kai |
| author_sort | Khalifa, Othman Omran |
| building | IIUM Repository |
| collection | Online Access |
| description | Human posture recognition is gaining increasing attention in the field of computer vision due to its
promising applications in the areas of personal health care, environmental awareness, human-computerinteraction and surveillance systems. Human posture recognition in video sequences is a challenging task
which is part of the more general problem of video sequence interpretation. In this project, an intelligent
human posture recognition system using a single static camera is proposed. The project consists of two
stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system
is trained and evaluated using a dataset of human postures to ‘teach’ the system to classify human
postures for any future inputs. When the training and evaluation process is deemed satisfactory as
measured by recognition rates, the trained system is then deployed to recognize human postures in any
input video sequence. In the training stage, to obtain the human posture datasets, video sequences have
been recorded and preprocessed to extract human silhouettes. The training and testing were performed
using four different classifiers which are Multilayer Perceptron Feedforward Neural networks, SelfOrganizing Maps, Fuzzy C Means and K Means. The recognition rates (accuracies) of those classifiers
were then compared and results indicate that MLP gives the highest. Performance comparisons between
the proposed systems and existing systems were also carried out. |
| first_indexed | 2025-11-14T15:12:30Z |
| format | Proceeding Paper |
| id | iium-23110 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:12:30Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-231102013-06-19T03:59:28Z http://irep.iium.edu.my/23110/ Intelligent human posture recognition in video sequences Khalifa, Othman Omran Htike Ali, Kyaw Kyaw Lai, Weng Kai TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Human posture recognition is gaining increasing attention in the field of computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computerinteraction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more general problem of video sequence interpretation. In this project, an intelligent human posture recognition system using a single static camera is proposed. The project consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using a dataset of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. In the training stage, to obtain the human posture datasets, video sequences have been recorded and preprocessed to extract human silhouettes. The training and testing were performed using four different classifiers which are Multilayer Perceptron Feedforward Neural networks, SelfOrganizing Maps, Fuzzy C Means and K Means. The recognition rates (accuracies) of those classifiers were then compared and results indicate that MLP gives the highest. Performance comparisons between the proposed systems and existing systems were also carried out. 2010 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/23110/1/p90.pdf Khalifa, Othman Omran and Htike Ali, Kyaw Kyaw and Lai, Weng Kai (2010) Intelligent human posture recognition in video sequences. In: IIUM Research, Innovation & Invention Exhibition (IRIIE 2010), 26 - 27 January 2010, Kuala Lumpur. http://www.iium.edu.my/irie/10/ |
| spellingShingle | TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Khalifa, Othman Omran Htike Ali, Kyaw Kyaw Lai, Weng Kai Intelligent human posture recognition in video sequences |
| title | Intelligent human posture recognition in video sequences |
| title_full | Intelligent human posture recognition in video sequences |
| title_fullStr | Intelligent human posture recognition in video sequences |
| title_full_unstemmed | Intelligent human posture recognition in video sequences |
| title_short | Intelligent human posture recognition in video sequences |
| title_sort | intelligent human posture recognition in video sequences |
| topic | TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| url | http://irep.iium.edu.my/23110/ http://irep.iium.edu.my/23110/ http://irep.iium.edu.my/23110/1/p90.pdf |