Human posture recognition: methodology and implementation

Htike, Kyaw Kyaw and Khalifa, Othman Omran (2015) Human posture recognition: methodology and implementation. Journal of Electrical Engineering Technology, 10 (4). pp. 1911-1915. ISSN 2093-7423

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Abstract

Human posture recognition is an attractive and challenging topic in 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 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 datasets 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. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

Item Type:	Article (Journal)
Additional Information:	4119/43103
Uncontrolled Keywords:	Posture recognition, Human activities, Intelligent classifiers.
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:	17 Jun 2015 11:34
Last Modified:	27 Oct 2020 08:52
URI:	http://irep.iium.edu.my/id/eprint/43103

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