Citation

Tham, Jie Sheng (2016) Computer Vision Techniques for Detection and Recognition of Drinking Activity. Masters thesis, Multimedia University.

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Abstract

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.

Item Type:	Thesis (Masters)
Additional Information:	Call No.: QA76.9.A48 T43 2016
Uncontrolled Keywords:	Ambient intelligence
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	21 May 2018 15:40
Last Modified:	21 May 2018 15:40
URII:	http://shdl.mmu.edu.my/id/eprint/7149

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