Comparison of supervised and unsupervised learning classifiers for human posture recognition

Human posture recognition is gaining increasing attention in the fields of artificial intelligence and 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 v...

Full description

Bibliographic Details
Main Authors:	Htike, Kyaw Kyaw, Khalifa, Othman Omran
Format:	Conference or Workshop Item
Language:	English
Published:	2010
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/5911/ http://irep.iium.edu.my/5911/ http://irep.iium.edu.my/5911/ http://irep.iium.edu.my/5911/1/05556749.pdf

id	iium-5911
recordtype	eprints
spelling	iium-59112011-11-25T08:36:47Z http://irep.iium.edu.my/5911/ Comparison of supervised and unsupervised learning classifiers for human posture recognition Htike, Kyaw Kyaw Khalifa, Othman Omran T Technology (General) Human posture recognition is gaining increasing attention in the fields of artificial intelligence and 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 comprehensive problem of video sequence interpretation. In this paper, an intelligent human posture recognition system in video sequences is proposed. Firstly, the system was trained and evaluated to classify five different human postures using both supervised and unsupervised learning classifiers. The supervised classifier used was Multilayer Perceptron Feedforward Neural Networks (MLP) whilst for unsupervised learning classifiers, Self Organizing Maps (SOM), Fuzzy C Means (FCM) and K Means have been employed. Results indicate that MLP performs (96% accuracy) much better than SOMs, FCM and K Means which give accuracies of 86%, 33% and 31% respectively. Secondly, all the classifiers were then trained and evaluated again to classify two postures. With only 2 postures, the accuracies of all the classifiers have increased dramatically, especially for unsupervised classifiers. This shows that supervised learning classifiers are superior to unsupervised ones for the task of human posture recognition and that the unsupervised classifiers do not learn very well for cases where a lot of postures have to be learnt as compared to the supervised learning classifier which gives high accuracy in either case. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/5911/1/05556749.pdf Htike, Kyaw Kyaw and Khalifa, Othman Omran (2010) Comparison of supervised and unsupervised learning classifiers for human posture recognition. In: International Conference on Computer and Communication Engineering (ICCCE 2010), 11-13 May 2010, Kuala Lumpur. http://www.iium.edu.my/iccce/10/ doi:10.1109/ICCCE.2010.5556749
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Htike, Kyaw Kyaw Khalifa, Othman Omran Comparison of supervised and unsupervised learning classifiers for human posture recognition
description	Human posture recognition is gaining increasing attention in the fields of artificial intelligence and 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 comprehensive problem of video sequence interpretation. In this paper, an intelligent human posture recognition system in video sequences is proposed. Firstly, the system was trained and evaluated to classify five different human postures using both supervised and unsupervised learning classifiers. The supervised classifier used was Multilayer Perceptron Feedforward Neural Networks (MLP) whilst for unsupervised learning classifiers, Self Organizing Maps (SOM), Fuzzy C Means (FCM) and K Means have been employed. Results indicate that MLP performs (96% accuracy) much better than SOMs, FCM and K Means which give accuracies of 86%, 33% and 31% respectively. Secondly, all the classifiers were then trained and evaluated again to classify two postures. With only 2 postures, the accuracies of all the classifiers have increased dramatically, especially for unsupervised classifiers. This shows that supervised learning classifiers are superior to unsupervised ones for the task of human posture recognition and that the unsupervised classifiers do not learn very well for cases where a lot of postures have to be learnt as compared to the supervised learning classifier which gives high accuracy in either case.
format	Conference or Workshop Item
author	Htike, Kyaw Kyaw Khalifa, Othman Omran
author_facet	Htike, Kyaw Kyaw Khalifa, Othman Omran
author_sort	Htike, Kyaw Kyaw
title	Comparison of supervised and unsupervised learning classifiers for human posture recognition
title_short	Comparison of supervised and unsupervised learning classifiers for human posture recognition
title_full	Comparison of supervised and unsupervised learning classifiers for human posture recognition
title_fullStr	Comparison of supervised and unsupervised learning classifiers for human posture recognition
title_full_unstemmed	Comparison of supervised and unsupervised learning classifiers for human posture recognition
title_sort	comparison of supervised and unsupervised learning classifiers for human posture recognition
publishDate	2010
url	http://irep.iium.edu.my/5911/ http://irep.iium.edu.my/5911/ http://irep.iium.edu.my/5911/ http://irep.iium.edu.my/5911/1/05556749.pdf
first_indexed	2023-09-18T20:14:43Z
last_indexed	2023-09-18T20:14:43Z
_version_	1777407711005114368

Comparison of supervised and unsupervised learning classifiers for human posture recognition

Similar Items