Drinking Behavior Detection

Using both Static and Dynamic information

Master thesis (2019)

Authors

X. Teng Electrical Engineering, Mathematics and Computer Science

Contributors

H.S. Hung Pattern Recognition and Bioinformatics - EEMCS (mentor)
L.C. Cabrera Quiros Pattern Recognition and Bioinformatics - EEMCS (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2019 Xiang Teng
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Published Date

18-06-2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This work gives a method which can use both motion and shape information for drinking action detection. Drinking action is differ from the other gestures. A complete drinking action duration could be divided into 3 different stages. Motion-based features and shape-based features are complement with each other in each stage. By using this feature, we have defined a fusion strategy that can use both the advantage of the strength of each part at a different stage.