Long Range Automated Persistent Surveillance

Acknowledgement First and foremost, I am deeply indebted to my parents, Zhongqing Jin and Qi Yao, who have always encouraged me to pursue higher education. To my husband, Yanzhen Li, who has always supported me during this journey. To my sister, Lan Jin, and her family for their love. I would like to thank my advisor, Dr. Mongi Abidi, who has made me who I am and who I will be in my professional career. His willingness to support my work and his guidance throughout my studies has allowed me to develop my abilities to think through the problems and skills to find the answers. I thank him for that opportunity. Special thanks indeed go to Dr. Besma Abidi, whose regular technical comments provided me valuable guidance throughout my research. Also, I would like to thank Dr. Paul Crilly, Dr. Hairong Qi, and Dr. Hamparsum Bozdogan. Their advice and counsel have been of equal importance. I greatly appreciate their time and input to this dissertation. Within the IRIS group, to Dr. David Page and Dr. Andreas Koschan I express my sincerest gratitude for the opportunity and many conversations that have had tremendous impacts on my research. I owe many thanks to my fellow graduate students, Abstract This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. For an automated and persistent surveillance, a sufficient overlap of the camera's field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera's field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera's zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target's 3D motion, including off-plane rotations. The effectiveness of the scale estimation …