轉載自：http://blog.sciencenet.cn/blog-722391-571072.html
以前做過一些關於背景建模，運動目標檢測的工作，打算進行一下小結，那麼就先從這篇CVPR2011這篇評測的文章說起吧。Evaluation
of Background Subtraction Techniques for Video Surveillance (PDF)
Sebastian Brutzer,
Benjamin Hoeferlin (University of Stuttgart), Gunther Heidemann (University of Stuttgart)這篇文章的項目首頁：http://www.vis.uni-stuttgart.de/index.php?id=sabs可以在這個網頁上下載最新的資料庫，以及一些評測的代碼（注意是評測的代碼，不是背景建模方法的代碼）。這篇文章對近年來背景建模的一些方法做了一些比較，比較的方法有：
本人感覺這篇文章之所以能夠發在CVPR這種進階別的會議上，主要有一下原因：1. 作者公開發布了一個資料，而且這個資料庫是合成，所以比較方便用來量化評價其他方法；2.表面上工作量很大，之所以說表面上工作量很大，作者雖然比較了9中方法，但是這些方法在網上幾乎都有原始碼（整合在opencv中的），還有一些是已經公開了可執行程式的了。而作者的工作量不是很大。我們可以看Features那一列，基本上都是color為特徵，而作者忽略了紋理特徵的背景建模。紋理特徵奧魯大學做LBP的人在06年就發表了用紋理做背景建模的文章，而且是發表在PAMI上面的，作者不能不知道吧，試問他比較的這九中方法那種發表在PAMI上了。再說2010
CVPR上面有一篇Stan Li的文章也是用紋理做，兩篇文章效果都很好。也不知道作者為什麼沒有比較... ...以後我們會介紹這兩個經典的方法的；3.分析的還可以，貌似所有cvpr的文章分析的都不錯。
作者分析了背景建模有以下的痛點：
- Gradual
illumination changes: It is desirable that background model adapts to gradual changes of the appearance of the environment. For example in outdoor settings, the light intensity typically varies during day.
- Sudden illumination changes: Sudden once-off changes are not covered by the background model. They occur for example with sudden switch of light, strongly
affect the appearance of background, and cause false positive detections.
- Dynamic background: Some parts of the scenery may contain movement, but should be regarded as background, according to their relevance. Such movement can
be periodical or irregular (e.g., traffic lights, waving trees).
- Camouflage: Intentionally or not, some objects may poorly differ from the appearance of background, making correct classification difficult. This is especially
important in surveillance applications.
- Shadows: Shadows cast by foreground objects often complicate further processing steps subsequent to background subtraction. Overlapping shadows of foreground
regions for example hinder their separation and classification. Hence, it is preferable to ignore these irrelevant regions.
- Bootstrapping: If initialization data which is free from foreground objects is not available, the background model has to be initialized using a bootstrapping
strategy.
- Video noise: Video signal is generally superimposed by noise. Background subtraction approaches for video surveillance have to cope with such degraded signals
affected by different types of noise, such as sensor noise or compression artifacts.
評測的結果：
值得注意的是，Barnich 方法速度效能都很不錯，他的文章中有虛擬碼，作者的首頁上提供可執行程式，並且可以整合到自己的程式中。
