Publication Date
2013
Document Type
Dissertation
Committee Members
Nikolaos Bourbakis (Advisor), Soon Chung (Committee Member), Ioannis Hatziligeroudis (Committee Member), Yong Pei (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
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.
Page Count
165
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2013
Copyright
Copyright 2013, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.
