Drinking Behavior Detection

Using both Static and Dynamic information

Master Thesis (2019)
Author(s)

X. Teng (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

HS Hung – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

L.C. Cabrera-Quiros – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2019 Xiang Teng
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Xiang Teng
Graduation Date
18-06-2019
Awarding Institution
Delft University of Technology
Faculty
Electrical Engineering, Mathematics and Computer Science
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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.

Files

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