Nikolaos Bourbakis (Advisor), Soon Chung (Committee Member), Mateen Rizki (Committee Member), George Tsihrintzis (Committee Member)
Doctor of Philosophy (PhD)
Human Activity Recognition is an actively researched domain for the past few decades, and is one of the most eminent applications of today. It is already part of our life, but due to high level of uncertainty and challenges of human detection, we have only application specific solutions. Thus, the problem being very demanding and still remains unsolved. Within this PhD we delve into the problem, and approach it from a variety of viewpoints. At start, we present and evaluate different architectures and frameworks for activity recognition. Henceforward, the focal point of our attention is automatic human activity recognition. We conducted and present a survey that compares, categorizes, and evaluates research surveys and reviews into four categories. Then a novel fully automatic view-independent multi-formal languages collaborative scheme is presented for complex activity and emotion recognition, which is the main contribution of this dissertation. We propose a collaborative three formal-languages, that is responsible for parsing manipulating, and understanding all the data needed. Artificial Neural Networks are used to classify an action primitive (simple activity), as well as to define change of activity. Finally, we capitalize the advantages of Fuzzy Cognitive Maps, and Rule-Based Colored Petri-Nets to be able to classify a sequence of activities as normal or ab-normal.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2018, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.