Background Subtraction Theory and Practice

Background subtraction is a widely used concept for detection of moving objects in videos. In the last two decades there has been a lot of development in designing algorithms for background subtraction, as well as wide use of these algorithms in various important applications, such as visual surveillance, sports video analysis, motion capture, etc. Various statistical approaches have been proposed to model scene backgrounds. The concept of background subtraction also has been extended to detect objects from videos captured from moving cameras. This book reviews the concept and practice of background subtraction. We discuss several traditional statistical background subtraction models, including the widely used parametric Gaussian mixture models and non-parametric models. We also discuss the issue of shadow suppression, which is essential for human motion analysis applications. This book discusses approaches and tradeoffs for background maintenance. This book also reviews many of the recent developments in background subtraction paradigm. Recent advances in developing algorithms for background subtraction from moving cameras are described, including motion-compensation-based approaches and motion-segmentation-based approaches. For links to the videos to accompany this book, please see sites.google.com/a/morganclaypool.com/backgroundsubtraction/ Table of Contents: Preface / Acknowledgments / Figure Credits / Object Detection and Segmentation in Videos / Background Subtraction from a Stationary Camera / Background Subtraction from a Moving Camera / Bibliography / Author's Biography

Preface.- Acknowledgments.- Figure Credits.- Object Detection and Segmentation in Videos.- Background Subtraction from a Stationary Camera.- Background Subtraction from a Moving Camera.- Bibliography.- Author's Biography .

Dr. Elgammal is an associate professor at the Department of Computer Science, Rutgers, the State University of New Jersey since Fall 2002. Dr. Elgammal is also a member of the Center for Computational Biomedicine Imaging and Modeling (CBIM). His primary research interest is computer vision and machine learning. His research focus includes human activity recognition, human motion analysis, tracking, human identification, and statistical methods for computer vision. Dr. Elgammal received the National Science Foundation CAREER Award in 2006. He has been the principal investigator and co-principal investigator of several research projects in the areas of human motion analysis, gait analysis, tracking, facial expression analysis and scene modeling; funded by NSF and ONR. Dr. Elgammal is a member of the review committee/board in several of the top conferences and journals in the computer vision field and is a senior IEEE member. Dr. Elgammal received his Ph.D. in 2002 and M.Sc. degree in 2000 in computer science from the University of Maryland, College Park. Dr. Elgammal received M.Sc. and B.E. degrees in computer science and automatic control from Alexandria University in 1996 and 1993, respectively.