Date of Award
12-2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering and Sciences
First Advisor
Thomas Eskridge
Second Advisor
Georgios Anagnostopoulos
Third Advisor
Marius Silaghi
Fourth Advisor
Phillip Bernhard
Abstract
The topic of Human Action Classification has attracted significant interest these past years, which can be attributed to the advancements in deep learning methodologies. Its applications range from robotics to surveillance and automated video categorization, as well as in the healthcare industry. The literature, however, has primarily focused on offline action classification, without significant attention being given to the constraints of an online real-time classifier. Using an intermediate skeletal representation of humans, while convoluted, provides a scalable means of tackling the action classification problem. This project discusses the existing literature, and adapts two of the state-of-the-art approaches for real-time analysis. An extensive analysis is performed to distinguish the advantages and drawbacks of each model. Lastly, an end-to-end system is implemented to experiment the efficacy of the classifiers in real-world environments.
Recommended Citation
Zisis Tegos, Kleanthis, "Real-Time Action Classification using Intermediate Skeletal Pose Estimation" (2020). Theses and Dissertations. 812.
https://repository.fit.edu/etd/812