| 期刊全稱 | Background Subtraction | | 期刊簡(jiǎn)稱 | Theory and Practice | | 影響因子2023 | Ahmed Elgammal | | 視頻video | http://file.papertrans.cn/181/180219/180219.mp4 | | 學(xué)科分類 | Synthesis Lectures on Computer Vision | | 圖書(shū)封面 |  | | 影響因子 | 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 approac | | Pindex | Book 2015 |
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