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

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.

背景差分是在影片中檢測移動物體的廣泛應用概念。在過去的二十年中，設計背景差分算法的發展非常迅速，並且廣泛應用於各種重要應用中，例如視覺監控、運動視頻分析、動作捕捉等。已經提出了各種統計方法來建模場景背景。背景差分的概念也已擴展到從移動攝像頭捕捉的視頻中檢測物體。本書回顧了背景差分的概念和實踐。我們討論了幾種傳統的統計背景差分模型，包括廣泛使用的參數化高斯混合模型和非參數化模型。我們還討論了陰影抑制的問題，這對於人體運動分析應用非常重要。本書討論了背景維護的方法和權衡。本書還回顧了背景差分範式的許多最新發展。描述了從移動攝像頭中開發背景差分算法的最新進展，包括基於運動補償的方法和基於運動分割的方法。