Background Subtraction: Theory and Practice (Synthesis Lectures on Computer Vision, Band 6)

Description

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 Characterization of Video Data The Space of Solutions Foreground Detection vs. Background Subtraction Video Segmentation and Motion Segmentation Background Subtraction Concept Background Subtraction from a Stationary Camera Introduction Challenges in Scene Modeling Probabilistic Background Modeling Parametric Background Models A Single Gaussian Background Model A Mixture Gaussian Background Model Non-Parametric Background Models Kernel Density Estimation (KDE) KDE Background Models KDE-Background Practice and Other Non-Parametric Models Other Background Models Predictive-Filtering Background Models Hidden Markov Model Background Subtraction Subspace Methods for Background Subtraction Neural Network Models Features for Background Modeling Shadow Suppression Color Spaces and Achromatic Shadows Algorithmic Approaches for Shadow Detection Tradeoffs in Background Maintenance Background Subtraction from a Moving Camera Difficulties in the Moving-Camera Case Motion-Compensation-Based Background-Subtraction Techniques Layered-Motion Segmentation Motion-Segmentation-Based Background-Subtraction Approaches Orthographic Camera – Factorization-Based Background Models Dense Bayesian Appearance Modeling Moving Away from the Affine Assumption – Manifold-Based Background Models Bibliography Author's Biography