运动检测（前景检测）之（一）ViBe

运动检测（前景检测）之（一）ViBe

[e-mail]

http://blog.csdn.net/zouxy09

 

       因为监控发展的需求，目前前景检测的研究还是很多的，也出现了很多新的方法和思路。个人了解的大概概括为以下一些：

       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

#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

#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 pixelm_foregroundMatchCount.at<uchar>(i, j) = 0;// Set background pixel to 0m_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 pixelm_foregroundMatchCount.at<uchar>(i, j)++;// Set background pixel to 255m_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, NUM_SAMPLES);if (random == 0){random = rng.uniform(0, NUM_SAMPLES);m_samples[random].at<uchar>(i, j) = _image.at<uchar>(i, j);}}}}}}

Main.cpp

// 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  : [e-mail]#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;}