Human posture recognition and classification

Khalifa, Othman Omran and Htike, Kyaw Kyaw (2013) Human posture recognition and classification. In: 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE), 26-28 Aug. 2013, Khartoum, Sudan.

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Abstract

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-computer-interaction 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. This paper presents a novel an intelligent human posture recognition system for video surveillance using a single static camera. The training and testing were performed using four different classifiers. The recognition rates (accuracies) of those classifiers were then compared and results indicate that MLP gives 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 postures trained and evaluated. Performance comparisons between the proposed systems and existing systems were also carried out.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	4119/33619 (Print ISBN: 978-1-4673-6231-3, DOI: 10.1109/ICCEEE.2013.6633905)
Uncontrolled Keywords:	Human Postures, Human Behavior Recognition, Pattern Recognition.
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr Othman O. Khalifa
Date Deposited:	27 Dec 2013 16:25
Last Modified:	27 Dec 2013 16:35
URI:	http://irep.iium.edu.my/id/eprint/33619

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