Computer Vision Techniques for Detection and Recognition of Drinking Activity

This thesis presents two novel computer vision techniques for detection and recognition of drinking activities at home which utilise only the depth information from RGBD cameras. According to my best understanding, there is very little work on using video cameras with depth sensor for the detection...

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Bibliographic Details
Main Author:	Tham, Jie Sheng
Format:	Thesis
Published:	2016
Subjects:	QA75.5-76.95 Electronic computers Computer science
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id	my-mmu-ep.7149
record_format	uketd_dc
spelling	my-mmu-ep.71492018-05-21T15:40:28Z Computer Vision Techniques for Detection and Recognition of Drinking Activity 2016-08 Tham, Jie Sheng QA75.5-76.95 Electronic computers. Computer science This thesis presents two novel computer vision techniques for detection and recognition of drinking activities at home which utilise only the depth information from RGBD cameras. According to my best understanding, there is very little work on using video cameras with depth sensor for the detection and recognition of ambient assisted living dining activity. The main advantage of using depth information is that the accuracy will not be affected by the change of lighting condition and illumination, as compared with using the conventional RGB cameras. In particular, the ﬁrst proposed technique extracts the features from the depth information of hand action characteristic during the drinking. As the drinking action features are gathered, dynamic time warping algorithm is used to recognise and detect the drinking activity. The experimental results show that the proposed method has a comparatively high recognition accuracy of 89% in comparison with the existing visual-based techniques. 2016-08 Thesis http://shdl.mmu.edu.my/7149/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php masters Multimedia University Faculty of Engineering
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Tham, Jie Sheng Computer Vision Techniques for Detection and Recognition of Drinking Activity
description	This thesis presents two novel computer vision techniques for detection and recognition of drinking activities at home which utilise only the depth information from RGBD cameras. According to my best understanding, there is very little work on using video cameras with depth sensor for the detection and recognition of ambient assisted living dining activity. The main advantage of using depth information is that the accuracy will not be affected by the change of lighting condition and illumination, as compared with using the conventional RGB cameras. In particular, the ﬁrst proposed technique extracts the features from the depth information of hand action characteristic during the drinking. As the drinking action features are gathered, dynamic time warping algorithm is used to recognise and detect the drinking activity. The experimental results show that the proposed method has a comparatively high recognition accuracy of 89% in comparison with the existing visual-based techniques.
format	Thesis
qualification_level	Master's degree
author	Tham, Jie Sheng
author_facet	Tham, Jie Sheng
author_sort	Tham, Jie Sheng
title	Computer Vision Techniques for Detection and Recognition of Drinking Activity
title_short	Computer Vision Techniques for Detection and Recognition of Drinking Activity
title_full	Computer Vision Techniques for Detection and Recognition of Drinking Activity
title_fullStr	Computer Vision Techniques for Detection and Recognition of Drinking Activity
title_full_unstemmed	Computer Vision Techniques for Detection and Recognition of Drinking Activity
title_sort	computer vision techniques for detection and recognition of drinking activity
granting_institution	Multimedia University
granting_department	Faculty of Engineering
publishDate	2016
_version_	1747829653830107136

Computer Vision Techniques for Detection and Recognition of Drinking Activity

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