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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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 |
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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 |