基于OpenCV的多目标动态检测与跟踪

1、源代码

#include "stdafx.h"
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv/cv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <time.h>
#include <math.h>
#include <ctype.h>
#include <stdio.h>
#include <string.h>
#include<windows.h>
#include <mmsystem.h>

//跟踪参数
const double MHI_DURATION = 0.5;//最大跟踪时间
const double MAX_TIME_DELTA = 0.5;
const double MIN_TIME_DELTA = 0.05;
const int N = 3;
const int CONTOUR_MAX_AERA = 100;//矩形面积

IplImage **buf = 0;
int last = 0;
int flag;
IplImage *mhi = 0; // MHI: motion history image
CvConnectedComp *cur_comp, min_comp;
CvConnectedComp comp;
CvMemStorage *storage;
CvPoint pt[4];
// img – 输入视频帧
// dst – 检测结果
void update_mhi(IplImage* img, IplImage* dst, int diff_threshold)
{
	double timestamp = clock() / 100.; //获取当前时间
	CvSize size = cvSize(img->width, img->height);
	int i, idx1, idx2;
	IplImage* silh;
	IplImage* pyr = cvCreateImage(cvSize((size.width & -2) / 2, (size.height & -2) / 2), 8, 1);
	CvMemStorage *stor;
	CvSeq *cont;
	if (!mhi || mhi->width != size.width || mhi->height != size.height)
	{
		if (buf == 0)
		{
			buf = (IplImage**)malloc(N*sizeof(buf[0]));//动态内存分配
			memset(buf, 0, N*sizeof(buf[0]));
		}

		for (i = 0; i < N; i++)
		{
			cvReleaseImage(&buf[i]);
			buf[i] = cvCreateImage(size, IPL_DEPTH_8U, 1);
			cvZero(buf[i]);
		}
		cvReleaseImage(&mhi);
		mhi = cvCreateImage(size, IPL_DEPTH_32F, 1);
		cvZero(mhi);
	}
	cvCvtColor(img, buf[last], CV_BGR2GRAY); //rgb->gray
	idx1 = last;
	idx2 = (last + 1) % N;
	last = idx2;
	// 做帧差
	silh = buf[idx2];
	cvAbsDiff(buf[idx1], buf[idx2], silh); //两帧差异
	// 对差图像做二值化
	cvThreshold(silh, silh, 30, 255, CV_THRESH_BINARY); //src(x,y)>threshold ,dst(x,y) = max_value; 否则,dst（x,y）=0;

	cvUpdateMotionHistory(silh, mhi, timestamp, MHI_DURATION); //更新像素点的运动历史
	cvCvtScale(mhi, dst, 255. / MHI_DURATION,
		(MHI_DURATION - timestamp)*255. / MHI_DURATION);//timestamp是时间戳;MHI_DURATION，获得的是当前时间
	cvCvtScale(mhi, dst, 255. / MHI_DURATION, 0);

	// 中值滤波，消除小的噪声
	cvSmooth(dst, dst, CV_MEDIAN, 3, 0, 0, 0);

	// 向下采样，去掉噪声
	cvPyrDown(dst, pyr, 7);
	cvDilate(pyr, pyr, 0, 1); // 做膨胀操作，消除目标的不连续空洞
	cvPyrUp(pyr, dst, 7);
	//
	// 下面的程序段用来找到轮廓
	//
	// Create dynamic structure and sequence.
	stor = cvCreateMemStorage(0);
	cont = cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvSeq), sizeof(CvPoint), stor);

	// 找到所有轮廓
	cvFindContours(dst, stor, &cont, sizeof(CvContour),
		CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0, 0));
	// 直接使用CONTOUR中的矩形来画轮廓
	for (; cont; cont = cont->h_next)
	{
		flag = 0;
		CvRect r = ((CvContour*)cont)->rect;
		if (r.height * r.width > CONTOUR_MAX_AERA)
		{
			cvRectangle(img, cvPoint(r.x, r.y),
				cvPoint(r.x + r.width, r.y + r.height),
				CV_RGB(255, 0, 0), 1, CV_AA, 0);
			flag = 1;
		}
		else
		{
			flag = 0;
		}
	}
	cvReleaseMemStorage(&stor);
	cvReleaseImage(&pyr);
}

int main(int argc, char** argv)
{
	IplImage* motion = 0;
	CvCapture* capture = 0; //视频获取结构 
	
	while (1){
		//capture = cvCreateCameraCapture(0);//读摄像头视频
		capture = cvCreateFileCapture("C:\\Users\\Lijunliang\\Desktop\\0.5X.avi");    //读本地视频文件
		if (capture)
		{
			cvNamedWindow("Motion", 1);
			for (;;)
			{
				SYSTEMTIME sys;//获取当前系统时间
				GetLocalTime(&sys);
				char* t_y = new char[128];
				sprintf(t_y, ("%4.4d-%2.2d-%2.2d %2.2d:%2.2d:%2.2d"),

					sys.wYear, sys.wMonth, sys.wDay,
					sys.wHour, sys.wMinute, sys.wSecond);

				CvFont font;
				cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX, 0.5, 0.5, 0, 2, 8);
				IplImage* image;
				if (!cvGrabFrame(capture)) //从摄像头或者视频文件中抓取帧
					break;
				image = cvRetrieveFrame(capture); //取回由函数cvGrabFrame抓取的图像,返回由函数cvGrabFrame 抓取的图像的指针
				if (image)
				{
					if (!motion)
					{
						motion = cvCreateImage(cvSize(image->width, image->height), 8, 1);
						cvZero(motion);
						motion->origin = image->origin; ///* 0 - 顶—左结构, 1 - 底—左结构 (Windows bitmaps 风格) */
					}
				}
				update_mhi(image, motion, 6);
				cvPutText(image, t_y, cvPoint(10, 25), &font, CV_RGB(255, 0, 0));
				cvShowImage("Motion", image);
				if (cvWaitKey(10) >= 0)
					break;
			}
			cvReleaseCapture(&capture);
			cvDestroyWindow("Motion");
		}
	}
	return 0;
}

2、实验结果

注：使用的检测源文件是是基于solidworks下路径配合做的动画视频，目的是避免噪声干扰和前景干扰，也就是在相当理想的情况环境下进行检测与跟踪，由结果可知检测与跟踪结果完美。使用者可以结合自己的使用对源程序稍加修改。