Chaz Chang

Publication Date

Fall 2023

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Chris Tseng

Second Advisor

Nada Attar

Third Advisor

Thomas Austin

Keywords

Gesture Recognition, Pose Estimation, Machine Learning, Computer Vision, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)

Abstract

Gesture recognition is a machine learning and computer vision application where gestures are detected from videos. This project uses pose estimation to find the coordinates of important joints as a preprocessing step before trying to classify the gesture. Machine learning layers such as Convolutional Neural Network and Long Short-Term Memory are used. Various types of machine learning models are trained. The accuracy and f1 score of each model are compared. Feature selection is done by testing with different subsets of features. The results show that pose estimation as a preprocessing step provides good accuracy for gesture recognition. The results also show that the polar angle and velocity are more important than polar acceleration in recognizing the gesture.

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