Robust Video Object Tracking via Camera Self-Calibration - My Ph.D. Defense Experience

I am pleased to announce that I have successfully passed my Ph.D. Dissertation Defense in the Department of Electrical and Computer Engineering (ECE) at the University of Washington (UW). This has been a long and challenging journey, and I am grateful for the support of my supervisory committee, colleagues, sponsors, family, friends, and my faith.

My dissertation, entitled “Robust Video Object Tracking via Camera Self-Calibration,” focuses on improving the accuracy and robustness of video object tracking. Video object tracking is a crucial task in computer vision that involves identifying and tracking an object of interest in a video sequence. It has numerous applications, such as surveillance, autonomous driving, and virtual reality.

However, video object tracking is a challenging task due to various factors such as occlusions, illumination changes, and camera motion. In my dissertation, I propose a novel approach to video object tracking that utilizes camera self-calibration to enhance its robustness and accuracy. Camera self-calibration is the process of estimating the intrinsic and extrinsic parameters of a camera from a set of images without the need for external calibration tools.

My supervisory committee consisted of Prof. Jenq-Neng Hwang (Chair), Prof. Kenneth P. Bube (GSR), Prof. Linda G. Shapiro, Prof. Ming-Ting Sun, and Prof. Fa-Long Luo. I am grateful for their guidance, feedback, and support throughout my Ph.D. journey. Their expertise and insights were invaluable in shaping my research and helping me navigate the challenges of graduate school.

In conclusion, I am thrilled to have successfully defended my Ph.D. dissertation and am excited to continue exploring the exciting field of computer vision. Once again, I would like to express my heartfelt gratitude to my committee, colleagues, sponsors, family, friends, and my faith for their unwavering support and encouragement throughout my Ph.D. journey.