背景建模(一) Evaluation of Background Subtraction Techniques
转载自：http://blog.sciencenet.cn/blog-722391-571072.html
以前做过一些关于背景建模，运动目标检测的工作，打算进行一下小结，那么就先从这篇CVPR2011这篇评测的文章说起吧。Evaluation of Background Subtraction Techniques for Video Surveillance (PDF)
Sebastian Brutzer, Benjamin Hoeferlin (University of Stuttgart), Gunther Heidemann (University of Stuttgart)
这篇文章的项目主页： http://www.vis.uni-stuttgart.de/index.php?id=sabs
可以在这个网页上下载最新的数据库，以及一些评测的代码（注意是评测的代码，不是背景建模方法的代码）。
这篇文章对近年来背景建模的一些方法做了一些比较，比较的方法有：
本人感觉这篇文章之所以能够发在CVPR这种高级别的会议上，主要有一下原因：
1. 作者公开发布了一个数据，而且这个数据库是合成，所以比较方便用来量化评价其他方法；
2.
表面上工作量很大，之所以说表面上工作量很大，作者虽然比较了9中方法，但是这些方法在网上几乎都有源代码（集成在opencv中的），还有一些是已经公开了可执行程序的了。而作者的工作量不是很大。我们可以看Features那一列，基本上都是color为特征，而作者忽略了纹理特征的背景建模。纹理特征奥鲁大学做LBP的人在06年就发表了用纹理做背景建模的文章，而且是发表在PAMI上面的，作者不能不知道吧，试问他比较的这九中方法那种发表在PAMI上了。再说2010 CVPR上面有一篇Stan Li的文章也是用纹理做，两篇文章效果都很好。
也不知道作者为什么没有比较... ...以后我们会介绍这两个经典的方法的；
3.分析的还可以，貌似所有cvpr的文章分析的都不错。
作者分析了背景建模有以下的难点：
- 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.
评测的结果：
