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 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