背景扣除的中值方差 [英] Median variance in background subtraction

问题描述

我在执行本文时遇到一些问题 移动观察者的统计背景扣除

。

问题1 p>

在第4.1节中，它谈到...从第一个分量计算整个图像的中值方差...

我对这些作者的意思感到困惑。

根据 Stauffer& Grimson 的论文Adaptive Background Mixture Models for Real-Time Tracking（1999），对于每个背景模型，方差被初始化（例如以值36），然后针对每个像素更新。是否应该对该框架的所有像素采用第一个模型的方差的中值？

  OR 
  

我们根据每个像素的强度值历史记录计算方差然后采取所有这些差异的 e dian。

问题2 ：

在第4.1节中我很难理解公式（12）

a）从 1 到 H + 1 是'i'吗？如果是，第（H + 1）个模型如何拟合方程？

i）刚刚在公式（13）之后， P（A_1 | B_（H + 1），M）被定义为。不应该 rho_（H + 1）= min（1，N_tot / N_max）而不是max，这可以使 P（A_1 | B_ （H + 1），M）（ - ）ve？

< （12）的P（A_1 | B_（H + 1），M）* P（B_（H + 1）| N）到P（A_1 | Z，M）

b）当H = 1时， P（A1 | Z，M）变为1？

我的实施是此处。

请在我在 MATLAB 文件中检查我的试用版https://sites.google.com/site/learningcomputervision/background-subtraction/grimsongmmrel =nofollow>网页。

解决方案

此处的细分： http ：//blog.damiles.com/2009/03/the-basics-of-background-substraction/

它也在opencv的书中。 / p>

在opencv2中的代码： opencv2中的背景减法

I am facing some issues in implementation of the paper Statistical Background Subtraction for a Mobile Observer.

Question 1:

In Section 4.1, it talks about "... the median variance is computed over the entire image from the first components ..."

I am confused what the authors actually mean by this.

According to Stauffer & Grimson's paper Adaptive Background Mixture Models for Real-Time Tracking(1999), for every background model a variance gets initialized (say with value 36) and then it gets updated for each pixel. Should the median of the first model's variance across all the pixels for that frame should be taken?

                  OR

We compute the variance for each pixel based on its history of intensity values of those which belong to the first model and then take median of all these variances.

Question 2:

I am facing difficulty in understanding equation (12) in section 4.1

a) Is 'i' from 1 to H+1? If yes, how does the (H+1)th model fits in the equation?

i) Just after equation (13), P(A_1 | B_(H+1),M) is defined. Shouldn't rho_(H+1) = min(1, N_tot/N_max) instead of max which could make P(A_1 | B_(H+1),M) (-)ve?

ii) For the (H+1)th model should we have P(A_1 | B_(H+1),M) * P(B_(H+1) | N) to P(A_1 | Z,M) for equation (12)?

b) when H=1, does P(A1|Z,M) becomes 1?

My implementation is here.

Please check my trial in the MATLAB files which I have mentioned in my webpage.

解决方案

Nice breakdown of it here: http://blog.damiles.com/2009/03/the-basics-of-background-substraction/

Its also in the opencv book.

code in opencv2 here: Background subtraction in opencv2

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