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Tsitsoulis, AthanasiosA Methodology for Extracting Human Bodies from Still Images
Doctor of Philosophy (PhD), Wright State University, 2013, Computer Science and Engineering PhD
Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach.

Committee:

Nikolaos Bourbakis, Ph.D. (Advisor); Soon Chung, Ph.D. (Committee Member); Yong Pei, Ph.D. (Committee Member); Ioannis Hatziligeroudis, Ph.D. (Committee Member)

Subjects:

Computer Engineering; Computer Science

Keywords:

image segmentation metric; human activity recognition; human body segmentation; monitoring and surveillance

Angeleas, AnargyrosA Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction
Doctor of Philosophy (PhD), Wright State University, 2018, Computer Science and Engineering 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.

Committee:

Nikolaos Bourbakis, Ph.D. (Advisor); Soon Chung, Ph.D. (Committee Member); Mateen Rizki, Ph.D. (Committee Member); George Tsihrintzis, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

Human Activity Recognition