Ph.D. Dissertation Defense “A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction” By Anargyros Angeleas

Monday, April 23, 11:30 am to 1:30 pm
Campus: 
Dayton
499 Joshi
Audience: 
Current Students
Faculty
Staff

Ph.D. Committee:  Drs. Nikolaos Bourbakis, Advisor, Mateen Rizki, Soon Chung, and Georgios Tsihrintzis (University of Piraeus, Greece)

ABSTRACT:

Human Activity Recognition is an actively researched area 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 view-points. Initially, we present and evaluate different architectures and frameworks for activity recognition. Henceforward, the focal point of our attention is automatic human activity recognition.

We have conducted and presented 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. In particular, we propose a collaborative scheme of three formal-languages, that is responsible for parsing manipulating, and understanding all the data needed. Artificial Neural Networks, as learning mechanism, 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 abnormal.

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