问题
I'm using OpenCV2.2 to implement moving objects detection with the method of Background Subtraction. And I use the Gaussian Mixture Model(GMM) method to model the background reference image.
I directly get the foreground pixels(or foreground mask) by using the class cv::BackgroundSubtractorMOG provided in OpenCV2.2. It's convenient but the foreground mask returned by cv::BackgroundSubtractorMOG is not as good as I expected. In addition, it seems that cv::BackgroundSubtractorMOG performs poorer than the method of GMM wrote in C language provided in OpenCV1.0.
The following is my code in OpenCV2.2:
cv::BackgroundSubtractorMOG mog;
mog(frame, fgMask, 0.01);
So, did I use the method in a wrong way?
By the way, does cv::BackgroundSubtractorMOG perform shadow removal on the foreground pixels?
Thank you very much.
回答1:
When you create mog, you are not defining any parameters, so it is created with default parameters. Here you have a description of each parameter, maybe is just that. Try with 3, 4 5 Gaussians.
This function does not perforn shadow-removal but you have this other function that does. Good luck!
回答2:
There are recent algorithms which remove backgrounds (detect foreground) far better than the standard GMM implementation in OpenCV.
For example, there is a block-based classifier cascade approach described in this journal article, along with its C++ based source code.
回答3:
F.X.'s answer on this thread gives sample parameters of
backgroundSubtractor = new BackgroundSubtractorMOG(3, 4, 0.8);
回答4:
I will recommend using the following settings to get started. Then you can start tuning your parameters:
cv::BackgroundSubtractorMOG2 mog;
mog(rawFrame,foregroundFrame,-1);
mog.set("nmixtures", 3);
mog.set("detectShadows",1);
In this example I set the MOG2 subtractor with 3 Gaussian mixtures. I also enabled shadow detection.
来源：https://stackoverflow.com/questions/10458633/how-to-use-cvbackgroundsubtractormog-in-opencv