OpenCV C++ optical flow method for moving target detection

What is optical flow

Optical flow (optical flow) is the instantaneous velocity of the pixel movement of a space moving object on the observation imaging plane.

The optical flow method uses the changes in the time domain of the pixels in the image sequence and the correlation between adjacent frames to find the correspondence between the previous frame and the current frame, thereby calculating the movement of objects between adjacent frames A method of information.

Usually, the instantaneous change rate of grayscale at a specific coordinate point on a two-dimensional image plane is defined as an optical flow vector.

In other words: optical flow is used to specify the movement speed of the movement pattern in the time-varying image, because when the object is moving, the brightness pattern of the corresponding point on the image is also moving. The apparent motion of this image brightness mode is optical flow.

Procedure description

// Program description: from the official sample program in the Samples folder under the OpenCV installation directory-using optical flow method for moving target detection
// Operating system: Windows 10 64bit
// Development language: C++
// IDE version: Visual Studio 2019
/ / OpenCV version: 4.20

/************************************************************************

• Copyright© 2011 Yang Xian
• All rights reserved.
• File: opticalFlow.cpp
• Brief: lk optical flow method for moving target detection
• Version: 1.0
• Author: Yang Xian
• Email: [email protected]
• Date: 2011/11/18
• History:
  ************************************************************************/

Code

#include <opencv2/video/video.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <iostream>
#include <cstdio>

using namespace std;
using namespace cv;

//-----------------------------------【全局函数声明】-----------------------------------------
//		描述：声明全局函数
//-------------------------------------------------------------------------------------------------
void tracking(Mat& frame, Mat& output);
bool addNewPoints();
bool acceptTrackedPoint(int i);

//-----------------------------------【全局变量声明】-----------------------------------------
//		描述：声明全局变量
//-------------------------------------------------------------------------------------------------
string window_name = "optical flow tracking";
Mat gray;	// 当前图片
Mat gray_prev;	// 预测图片
vector<Point2f> points[2];	// point0为特征点的原来位置，point1为特征点的新位置
vector<Point2f> initial;	// 初始化跟踪点的位置
vector<Point2f> features;	// 检测的特征
int maxCount = 500;	// 检测的最大特征数
double qLevel = 0.01;	// 特征检测的等级
double minDist = 10.0;	// 两特征点之间的最小距离
vector<uchar> status;	// 跟踪特征的状态，特征的流发现为1，否则为0
vector<float> err;

//-----------------------------------【main( )函数】--------------------------------------------
//		描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main()
{

	Mat frame;
	Mat result;

	VideoCapture capture(0);

	if (capture.isOpened())	// 摄像头读取文件开关
	{
		while (true)
		{
			capture >> frame;

			if (!frame.empty())
			{
				tracking(frame, result);
			}
			else
			{
				printf(" --(!) No captured frame -- Break!");
				break;
			}

			int c = waitKey(50);
			if ((char)c == 27)
			{
				break;
			}
		}
	}
	return 0;
}

//-------------------------------------------------------------------------------------------------
// function: tracking
// brief: 跟踪
// parameter: frame	输入的视频帧
//			  output 有跟踪结果的视频帧
// return: void
//-------------------------------------------------------------------------------------------------
void tracking(Mat& frame, Mat& output)
{

	//此句代码的OpenCV3版为：
	cvtColor(frame, gray, COLOR_BGR2GRAY);
	//此句代码的OpenCV2版为：
	//cvtColor(frame, gray, CV_BGR2GRAY);

	frame.copyTo(output);

	// 添加特征点
	if (addNewPoints())
	{
		goodFeaturesToTrack(gray, features, maxCount, qLevel, minDist);
		points[0].insert(points[0].end(), features.begin(), features.end());
		initial.insert(initial.end(), features.begin(), features.end());
	}

	if (gray_prev.empty())
	{
		gray.copyTo(gray_prev);
	}
	// l-k光流法运动估计
	calcOpticalFlowPyrLK(gray_prev, gray, points[0], points[1], status, err);
	// 去掉一些不好的特征点
	int k = 0;
	for (size_t i = 0; i < points[1].size(); i++)
	{
		if (acceptTrackedPoint(i))
		{
			initial[k] = initial[i];
			points[1][k++] = points[1][i];
		}
	}
	points[1].resize(k);
	initial.resize(k);
	// 显示特征点和运动轨迹
	for (size_t i = 0; i < points[1].size(); i++)
	{
		line(output, initial[i], points[1][i], Scalar(0, 0, 255));
		circle(output, points[1][i], 3, Scalar(0, 255, 0), -1);
	}

	// 把当前跟踪结果作为下一此参考
	swap(points[1], points[0]);
	swap(gray_prev, gray);

	imshow(window_name, output);
}

//-------------------------------------------------------------------------------------------------
// function: addNewPoints
// brief: 检测新点是否应该被添加
// parameter:
// return: 是否被添加标志
//-------------------------------------------------------------------------------------------------
bool addNewPoints()
{
	return points[0].size() <= 10;
}

//-------------------------------------------------------------------------------------------------
// function: acceptTrackedPoint
// brief: 决定哪些跟踪点被接受
// parameter:
// return:
//-------------------------------------------------------------------------------------------------
bool acceptTrackedPoint(int i)
{
	return status[i] && ((abs(points[0][i].x - points[1][i].x) + abs(points[0][i].y - points[1][i].y)) > 2);
}

running result

The camera video can be called, and the local video can also be read; the following is the camera capture video, the optical flow method tracking effect