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.
Recommended Citation
Chang, Chaz, "Gesture Recognition with Deep Learning" (2023). Master's Projects. 1327.
DOI: https://doi.org/10.31979/etd.t95t-4twh
https://scholarworks.sjsu.edu/etd_projects/1327
