学步园

下面这段代码利用OpenCV实现了最为简单的基于自适应背景更新的运动目标检测算法。

即：

运动前景提取——背景减除

foreground = |frame - background| > threshold 

更新背景模型——滑动平均滤波

background = background + (frame - background) *  alpha  = background * (1 - alpha) + frame * alpha 

//运动前景检测——基于自适应背景更新
//Author: www.icvpr.com
//Blog:   http://blog.csdn.net/icvpr

#include <iostream>
#include <string>

#include <opencv2/opencv.hpp>

int main(int argc, char** argv)
{
	std::string videoFileName = "../test.avi";

	int threshold = 25 ;    // 二值化阈值
	float alpha = 0.01;     // 更新速度 [0, 1]

	cv::VideoCapture capture;
	capture.open(videoFileName);
	if (!capture.isOpened())
	{
		std::cout<<"cannot open video"<<std::endl;
		return -1;
	}


	cv::Mat foregroundImg;
	cv::Mat foregroundMat;

	cv::Mat backgroundImg;
	cv::Mat backgroundMat;

	cv::Mat frame;
	cv::Mat grayImg;
	cv::Mat grayMat;
	
	while (capture.read(frame))
	{
		cv::cvtColor(frame, grayImg, CV_BGR2GRAY);
		grayImg.convertTo(grayMat, CV_32FC1);

		if (backgroundMat.empty())
		{
			grayImg.copyTo(backgroundImg);
			grayImg.convertTo(backgroundMat, CV_32FC1);		
		}
		
		// 背景减除
		cv::absdiff(grayMat, backgroundMat, foregroundMat);
		
		// 自适应背景更新
		cv::addWeighted(backgroundMat, alpha, foregroundMat, 1-alpha, 0, backgroundMat);

		// 二值化，获取前景像素点
		cv::threshold(foregroundMat, foregroundMat, threshold, 255, CV_THRESH_BINARY);


		// 为了显示用，将CV_32FC1转换为CV_8U
		cv::convertScaleAbs(foregroundMat, foregroundImg);
		cv::convertScaleAbs(backgroundMat, backgroundImg);

		cv::imshow("frame", frame);
		cv::imshow("foreground", foregroundImg);
		cv::imshow("background", backgroundImg);

		if (cv::waitKey(25) > 0)
		{
			break;
		}
	}

	return 0;
}

相关内容：www.icvpr.com

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