背景扣除中的中位数方差
我在实施论文时遇到一些问题移动观察者的统计背景扣除。
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的...”
我很困惑作者的实际含义。
根据斯托弗和Grimson 的论文“实时跟踪的自适应背景混合模型”（1999），对于每个背景模型，方差都会被初始化（例如值为 36），然后针对每个像素进行更新。是否应该采用该帧所有像素的第一个模型方差的中值？
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 m所有这些方差的 eedian。
问题 2：
我在理解第 4.1 节中的公式 (12) 时遇到困难
a) 'i' 是从 1 到 H+1 吗？如果是，第 (H+1) 个模型如何拟合方程？
i) 就在等式(13)之后，定义了P(A_1 | B_(H+1),M)。不应该用 rho_(H+1) = min(1, N_tot/N_max) 代替 max，这可能会使 P(A_1 | B_(H+1),M) > (-)ve?
ii) 对于第 (H+1) 个模型，我们应该将 P(A_1 | B_(H+1),M) * P(B_(H+1) | N) 转换为 P(A_1 | Z,M) ）对于方程（12）？
b) 当 H=1 时，P(A1|Z,M) 是否变为 1？
我的实现位于此处。
请在我的 网页。
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.
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（2）
问题 1，第一个选项，“是否应该采用该帧所有像素的第一个模型方差的中值？”。
是的，它假设背景像素的方差低于前景。因此，所有方差的中值将为背景提供可接受的方差。
Question 1, first option, "Should the median of first model's variance across all the pixels for that frame be taken?".
Yes, it assumes that the variance of the background pixel is lower than the foreground. So the median of all variances will give acceptable variance for the background.
这里有很好的细分：http://blog.damiles .com/2009/03/the-basics-of-background-substraction/
也在opencv书中。
opencv2中的代码在这里：opencv2中的背景减法
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