@incollection{ elgammal_background_2014,
  series = {Augmented {Vision} and {Reality}},
  title = {Background {Subtraction}: {Theory} and {Practice}},
  copyright = {©2014 Springer-Verlag Berlin Heidelberg},
  isbn = {978-3-642-37840-9, 978-3-642-37841-6},
  shorttitle = {Background {Subtraction}},
  url = {http://link.springer.com/chapter/10.1007/8612_2012_1},
  abstract = {Background subtraction is a widely-used concept utilized to detect moving objects in videos taken from a static camera. In the last two decades, several algorithms have been developed for background subtraction and were used in various important applications such as visual surveillance, sports video analysis, motion capture, etc. Various statistical approaches have been proposed to model scene backgrounds. In this chapter we review the concept and the practice in background subtraction. We discuss several basic statistical background subtraction models, including parametric Gaussian models and nonparametric models. We discuss the issue of shadow suppression, which is essential for human motion analysis applications. We also discuss approaches and tradeoffs for background maintenance. We also point out many of the recent developments in the background subtraction paradigm.},
  language = {en},
  number = {6},
  urldate = {2015-01-12TZ},
  booktitle = {Wide {Area} {Surveillance}},
  publisher = {Springer Berlin Heidelberg},
  author = {Elgammal, Ahmed},
  editor = {Asari, Vijayan K.},
  month = {January},
  year = {2014},
  note = {00008},
  pages = {1--21}
}

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