背景减法的中值方差 [英] Median variance in background subtraction

问题描述

我在论文的实施过程中遇到了一些问题移动观察者的统计背景减法.

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

问题 1:

在第 4.1 节中，它谈到...中值方差是根据第一个组件在整个图像上计算..."

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

我很困惑作者的实际意思.

I am confused what the authors actually mean by this.

根据 Stauffer &Grimson 的论文 Adaptive Background Mixture Models for Real-Time Tracking (1999)，对于每个背景模型，都会初始化一个方差(比如值为 36)，然后针对每个像素进行更新.是否应该取第一个模型在该帧所有像素上的方差的中位数?

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

我们根据属于第一个模型的像素的强度值历史计算每个像素的方差，然后取 me所有这些差异的数字.

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.

问题 2:

我在理解第 4.1 节中的方程 (12) 时遇到困难

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

a) 'i' 是从 1 到 H+1 吗?如果是，第 (H+1) 个模型如何适合方程?

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

i) 就在等式 (13) 之后，定义了 P(A_1 | B_(H+1),M).不应该 rho_(H+1) = min(1, N_tot/N_max) 而不是 max 可以使 P(A_1 | B_(H+1),M) (-)有吗?

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) 对于第 (H+1) 个模型，我们应该有 P(A_1 | B_(H+1),M) * P(B_(H+1) | N) 到 P(A_1 |Z,M) 等式(12)?

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) 当H=1时，P(A1|Z,M)会变成1吗?

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

我的实现是这里.

请检查我在 MATLAB 文件中的试用版="nofollow noreferrer">网页.

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

推荐答案

在这里很好地细分:http://blog.damiles.com/2009/03/the-basics-of-background-substraction/

opencv 书中也有.

Its also in the opencv book.

opencv2 中的代码:opencv2 中的背景减法

code in opencv2 here: Background subtraction in opencv2

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