帧差、背景减除（GMM、CodeBook、 SOBS、 SACON、 VIBE、 W4、多帧平均……）、光流（稀疏光流、稠密光流）、运动竞争（Motion Competition）、运动模版（运动历史图像）、时间熵……等等。如果加上他们的改进版，那就是很大的一个家族了。
对于上一些方法的一点简单的对比分析可以参考下：
http://www.cnblogs.com/ronny/archive/2012/04/12/2444053.html
至于哪个最好，看使用环境吧，各有千秋，有一些适用的情况更多，有一些在某些情况下表现更好。这些都需要针对自己的使用情况作测试确定的。呵呵。
推荐一个牛逼的库：http://code.google.com/p/bgslibrary/里面包含了各种背景减除的方法，可以让自己少做很多力气活。
还有王先荣博客上存在不少的分析：
http://www.cnblogs.com/xrwang/archive/2010/02/21/ForegroundDetection.html
下面的博客上转载王先荣的上面几篇，然后加上自己分析了两篇：
http://blog.csdn.net/stellar0
本文主要关注其中的一种背景减除方法：ViBe。stellar0的博客上对ViBe进行了分析，我这里就不再啰嗦了，具体的理论可以参考：
http://www2.ulg.ac.be/telecom/research/vibe/
http://blog.csdn.net/stellar0/article/details/8777283
http://blog.csdn.net/yongshengsilingsa/article/details/6659859
http://www2.ulg.ac.be/telecom/research/vibe/download.html
http://www.cvchina.info/2011/12/25/vibe/
《ViBe: A universal background subtraction algorithm for video sequences》
《ViBe: a powerful technique for background detection and subtraction in video sequences》
ViBe是一种像素级视频背景建模或前景检测的算法，效果优于所熟知的几种算法，对硬件内存占用也少，很简单。我之前根据stellar0的代码（在这里，非常感谢stellar0）改写成一个Mat格式的代码了，现在摆上来和大家交流，具体如下：（在VS2010+OpenCV2.4.2中测试通过）
ViBe.h
[cpp] view plain copy #pragma once #include <iostream> #include "opencv2/opencv.hpp" using namespace cv; using namespace std; #define NUM_SAMPLES 20 //每个像素点的样本个数 #define MIN_MATCHES 2 //#min指数 #define RADIUS 20 //Sqthere半径 #define SUBSAMPLE_FACTOR 16 //子采样概率 class ViBe_BGS { public: ViBe_BGS(void); ~ViBe_BGS(void); void init(const Mat _image); //初始化 void processFirstFrame(const Mat _image); void testAndUpdate(const Mat _image); //更新 Mat getMask(void){return m_mask;}; private: Mat m_samples[NUM_SAMPLES]; Mat m_foregroundMatchCount; Mat m_mask; };
ViBe.cpp
[cpp] view plain copy #include <opencv2/opencv.hpp> #include <iostream> #include "ViBe.h" using namespace std; using namespace cv; int c_xoff[9] = {-1, 0, 1, -1, 1, -1, 0, 1, 0}; //x的邻居点 int c_yoff[9] = {-1, 0, 1, -1, 1, -1, 0, 1, 0}; //y的邻居点 ViBe_BGS::ViBe_BGS(void) { } ViBe_BGS::~ViBe_BGS(void) { } /**************** Assign space and init ***************************/ void ViBe_BGS::init(const Mat _image) { for(int i = 0; i < NUM_SAMPLES; i++) { m_samples[i] = Mat::zeros(_image.size(), CV_8UC1); } m_mask = Mat::zeros(_image.size(),CV_8UC1); m_foregroundMatchCount = Mat::zeros(_image.size(),CV_8UC1); } /**************** Init model from first frame ********************/ void ViBe_BGS::processFirstFrame(const Mat _image) { RNG rng; int row, col; for(int i = 0; i < _image.rows; i++) { for(int j = 0; j < _image.cols; j++) { for(int k = 0 ; k < NUM_SAMPLES; k++) { // Random pick up NUM_SAMPLES pixel in neighbourhood to construct the model int random = rng.uniform(0, 9); row = i + c_yoff[random]; if (row < 0) row = 0; if (row >= _image.rows) row = _image.rows - 1; col = j + c_xoff[random]; if (col < 0) col = 0; if (col >= _image.cols) col = _image.cols - 1; m_samples[k].at<uchar>(i, j) = _image.at<uchar>(row, col); } } } } /**************** Test a new frame and update model ********************/ void ViBe_BGS::testAndUpdate(const Mat _image) { RNG rng; for(int i = 0; i < _image.rows; i++) { for(int j = 0; j < _image.cols; j++) { int matches(0), count(0); float dist; while(matches < MIN_MATCHES && count < NUM_SAMPLES) { dist = abs(m_samples[count].at<uchar>(i, j) - _image.at<uchar>(i, j)); if (dist < RADIUS) matches++; count++; } if (matches >= MIN_MATCHES) { // It is a background pixel m_foregroundMatchCount.at<uchar>(i, j) = 0; // Set background pixel to 0 m_mask.at<uchar>(i, j) = 0; // 如果一个像素是背景点，那么它有 1 / defaultSubsamplingFactor 的概率去更新自己的模型样本值 int random = rng.uniform(0, SUBSAMPLE_FACTOR); if (random == 0) { random = rng.uniform(0, NUM_SAMPLES); m_samples[random].at<uchar>(i, j) = _image.at<uchar>(i, j); } // 同时也有 1 / defaultSubsamplingFactor 的概率去更新它的邻居点的模型样本值 random = rng.uniform(0, SUBSAMPLE_FACTOR); if (random == 0) { int row, col; random = rng.uniform(0, 9); row = i + c_yoff[random]; if (row < 0) row = 0; if (row >= _image.rows) row = _image.rows - 1; random = rng.uniform(0, 9); col = j + c_xoff[random]; if (col < 0) col = 0; if (col >= _image.cols) col = _image.cols - 1; random = rng.uniform(0, NUM_SAMPLES); m_samples[random].at<uchar>(row, col) = _image.at<uchar>(i, j); } } else { // It is a foreground pixel m_foregroundMatchCount.at<uchar>(i, j)++; // Set background pixel to 255 m_mask.at<uchar>(i, j) = 255; //如果某个像素点连续N次被检测为前景，则认为一块静止区域被误判为运动，将其更新为背景点 if (m_foregroundMatchCount.at<uchar>(i, j) > 50) { int random = rng.uniform(0, SUBSAMPLE_FACTOR); if (random == 0) { random = rng.uniform(0, NUM_SAMPLES); m_samples[random].at<uchar>(i, j) = _image.at<uchar>(i, j); } } } } } }
Main.cpp
[cpp] view plain copy // This is based on // "VIBE: A POWERFUL RANDOM TECHNIQUE TO ESTIMATE THE BACKGROUND IN VIDEO SEQUENCES" // by Olivier Barnich and Marc Van Droogenbroeck // Author : zouxy // Date : 2013-4-13 // HomePage : http://blog.csdn.net/zouxy09 // Email : zouxy09@qq.com #include "opencv2/opencv.hpp" #include "ViBe.h" #include <iostream> #include <cstdio> using namespace cv; using namespace std; int main(int argc, char* argv[]) { Mat frame, gray, mask; VideoCapture capture; capture.open("video.avi"); if (!capture.isOpened()) { cout<<"No camera or video input!\n"<<endl; return -1; } ViBe_BGS Vibe_Bgs; int count = 0; while (1) { count++; capture >> frame; if (frame.empty()) break; cvtColor(frame, gray, CV_RGB2GRAY); if (count == 1) { Vibe_Bgs.init(gray); Vibe_Bgs.processFirstFrame(gray); cout<<" Training GMM complete!"<<endl; } else { Vibe_Bgs.testAndUpdate(gray); mask = Vibe_Bgs.getMask(); morphologyEx(mask, mask, MORPH_OPEN, Mat()); imshow("mask", mask); } imshow("input", frame); if ( cvWaitKey(10) == 'q' ) break; } return 0; }
原文地址：http://blog.csdn.net/zouxy09/article/details/9622285
