opencv中的GMM（混合高斯分布）算法原理及C++实现（BackgroundSubtractorMOG）_opencv c++ createbackgroundsubtractormog

1.opencv中的GMM算法

GMM（Gaussian Mixture Model）是一种经典的背景提取算法，opencv中也把它引入并封装为算法类。使用opencv2413版本时，通过BackgroundSubtractorMOG类即可调用。这里先给出一个调用例子和算法效果，代码如下所示。

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/video/background_segm.hpp>
 
 
int main()
{
	// Open the video file
    cv::VideoCapture capture("768X576.avi");
	// check if video successfully opened
	if (!capture.isOpened())
		return 0;
 
	// current video frame
	cv::Mat frame, frameGray; 
	// foreground binary image
	cv::Mat foreground;
 
	cv::namedWindow("Extracted Foreground");
 
	// The Mixture of Gaussian object
	// used with all default parameters
	cv::BackgroundSubtractorMOG mog;
 
	bool stop(false);
	// for all frames in video
	while (!stop) {
 
		// read next frame if any
		if (!capture.read(frame))
			break;
 
		// update the background
		// and return the foreground
		cvtColor(frame, frameGray, CV_BGR2GRAY);
		mog(frameGray,foreground,0.01);
 
		// show foreground
		cv::imshow("Extracted Foreground",foreground);
		cv::imshow("image",frame);
 
		// introduce a delay
		// or press key to stop
		if (cv::waitKey(10)>=0)
				stop= true;
	}
 
	return 0;
}

2.算法原理

BackgroundSubtractorMOG的实现在2413版本的opencv_2.4.13\opencv\sources\modules\video\src\bgfg_gaussmix.cpp中，cpp中讲到算法原理参考《An Improved Adaptive Background Mixture Model for Real-time Tracki