基于特征点的跟踪算法大致可以分为两个步骤：

1）探测当前帧的特征点；

2）通过当前帧和下一帧灰度比较，估计当前帧特征点在下一帧的位置；

3）过滤位置不变的特征点，余下的点就是目标了。

特征点包括：

1、Harris

2、SURF

3、FAST

4、STAR

5、SIFT

6、ORB

7、MSER

8、GETT

9、Dense

10、SimpleBlob

光流的基本方法：

1、基于梯度

2、基于匹配

3、基于能量

4、基于相位

光流法首先假设的条件：

1、亮度恒定

2、小运动

3、空间一致

使用函数：

goodFeaturesToTrack()——寻找图像中具有大特征的角点

calcOpticalFlowPyrLK()——计算一个稀疏特征集的光流

两篇博客链接：

Opencv学习笔记（九）光流法

http://blog.csdn.net/crzy_sparrow/article/details/7407604

【算法分析】Lucas–Kanade光流算法

http://www.cnblogs.com/gnuhpc/archive/2012/12/04/2802124.html

OpenCV3编程入门——光流法的代码

//#include "stdafx.h"#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>  // Gaussian Blur
#include <opencv2/ml/ml.hpp>
#include <opencv2/contrib/contrib.hpp>using namespace cv;
using namespace std;void duan_OpticalFlow(Mat &frame, Mat & result);
bool addNewPoints();
bool acceptTrackedPoint(int i);Mat curgray; // 当前图片
Mat pregray;    // 预测图片
vector<Point2f> point[2]; // point0为特征点的原来位置，point1为特征点的新位置
vector<Point2f> initPoint;    // 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;int main()
{Mat matSrc;Mat matRst;VideoCapture cap("Monkey.mp4");int totalFrameNumber = cap.get(CV_CAP_PROP_FRAME_COUNT);// perform the tracking processprintf("Start the tracking process, press ESC to quit.\n");for (int nFrmNum = 0; nFrmNum < totalFrameNumber; nFrmNum++) {// get frame from the videocap >> matSrc;if (!matSrc.empty()){duan_OpticalFlow(matSrc, matRst);cout << "This picture is " << nFrmNum << endl;}else{cout << "Error : Get picture is empty!" << endl;}if (waitKey(1) == 27) break;}waitKey(0);return 0;}void duan_OpticalFlow(Mat &frame, Mat & result)
{cvtColor(frame, curgray, CV_BGR2GRAY);frame.copyTo(result);if (addNewPoints()){goodFeaturesToTrack(curgray, features, maxCount, qLevel, minDist);point[0].insert(point[0].end(), features.begin(), features.end());initPoint.insert(initPoint.end(), features.begin(), features.end());}if (pregray.empty()){curgray.copyTo(pregray);}calcOpticalFlowPyrLK(pregray, curgray, point[0], point[1], status, err);int k = 0;for (size_t i = 0; i<point[1].size(); i++){if (acceptTrackedPoint(i)){initPoint[k] = initPoint[i];point[1][k++] = point[1][i];}}point[1].resize(k);initPoint.resize(k);for (size_t i = 0; i<point[1].size(); i++){line(result, initPoint[i], point[1][i], Scalar(0, 0, 255));circle(result, point[1][i], 3, Scalar(0, 255, 0), -1);}swap(point[1], point[0]);swap(pregray, curgray);imshow("Optical Flow Demo", result);//waitKey(50);
}bool addNewPoints()
{return point[0].size() <= 10;
}bool acceptTrackedPoint(int i)
{return status[i] && ((abs(point[0][i].x - point[1][i].x) + abs(point[0][i].y - point[1][i].y)) > 2);
}

====================OpenCV Source Code===============

enum
{OPTFLOW_USE_INITIAL_FLOW = CV_LKFLOW_INITIAL_GUESSES,OPTFLOW_LK_GET_MIN_EIGENVALS = CV_LKFLOW_GET_MIN_EIGENVALS,OPTFLOW_FARNEBACK_GAUSSIAN = 256
};//! constructs a pyramid which can be used as input for calcOpticalFlowPyrLK
CV_EXPORTS_W int buildOpticalFlowPyramid(InputArray img, OutputArrayOfArrays pyramid,Size winSize, int maxLevel, bool withDerivatives = true,int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT,bool tryReuseInputImage = true);//! computes sparse optical flow using multi-scale Lucas-Kanade algorithm
CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,InputArray prevPts, CV_OUT InputOutputArray nextPts,OutputArray status, OutputArray err,Size winSize=Size(21,21), int maxLevel=3,TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),int flags=0, double minEigThreshold=1e-4);//! computes dense optical flow using Farneback algorithm
CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next,CV_OUT InputOutputArray flow, double pyr_scale, int levels, int winsize,int iterations, int poly_n, double poly_sigma, int flags );//! estimates the best-fit Euqcidean, similarity, affine or perspective transformation
// that maps one 2D point set to another or one image to another.
CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst,bool fullAffine);//! computes dense optical flow using Simple Flow algorithm
CV_EXPORTS_W void calcOpticalFlowSF(Mat& from,Mat& to,Mat& flow,int layers,int averaging_block_size,int max_flow);CV_EXPORTS_W void calcOpticalFlowSF(Mat& from,Mat& to,Mat& flow,int layers,int averaging_block_size,int max_flow,double sigma_dist,double sigma_color,int postprocess_window,double sigma_dist_fix,double sigma_color_fix,double occ_thr,int upscale_averaging_radius,double upscale_sigma_dist,double upscale_sigma_color,double speed_up_thr);class CV_EXPORTS DenseOpticalFlow : public Algorithm
{
public:virtual void calc(InputArray I0, InputArray I1, InputOutputArray flow) = 0;virtual void collectGarbage() = 0;
};// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
//
// see reference:
//   [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
//   [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
CV_EXPORTS Ptr<DenseOpticalFlow> createOptFlow_DualTVL1();

==============================================

// Hand01.cpp : 定义控制台应用程序的入口点。
//#include "stdafx.h"#include <stdio.h>
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>  // Gaussian Blur
#include <opencv2/ml/ml.hpp>
#include <opencv2/contrib/contrib.hpp>#include <opencv2/video/tracking.hpp>using namespace cv;static void convertFlowToImage(const Mat &flow_x, const Mat &flow_y, Mat &img_x, Mat &img_y, double lowerBound, double higherBound) {
#define CAST(v, L, H) ((v) > (H) ? 255 : (v) < (L) ? 0 : cvRound(255*((v) - (L))/((H)-(L))))for (int i = 0; i < flow_x.rows; ++i) {for (int j = 0; j < flow_y.cols; ++j) {float x = flow_x.at<float>(i, j);float y = flow_y.at<float>(i, j);img_x.at<uchar>(i, j) = CAST(x, lowerBound, higherBound);img_y.at<uchar>(i, j) = CAST(y, lowerBound, higherBound);}}
#undef CAST
}static void drawOptFlowMap(const Mat& flow, Mat& cflowmap, int step, double, const Scalar& color)
{for (int y = 0; y < cflowmap.rows; y += step)for (int x = 0; x < cflowmap.cols; x += step){const Point2f& fxy = flow.at<Point2f>(y, x);line(cflowmap, Point(x, y), Point(cvRound(x + fxy.x), cvRound(y + fxy.y)),color);circle(cflowmap, Point(x, y), 2, color, -1);}
}int main(int argc, char** argv)
{// IO operationconst char* keys ={"{ f  | vidFile      | ex2.avi | filename of video }""{ x  | xFlowFile    | flow_x | filename of flow x component }""{ y  | yFlowFile    | flow_y | filename of flow x component }""{ i  | imgFile      | flow_i | filename of flow image}""{ b  | bound | 15 | specify the maximum of optical flow}"};//CommandLineParser cmd(argc, argv, keys);//string vidFile = cmd.get<string>("vidFile");//string xFlowFile = cmd.get<string>("xFlowFile");//string yFlowFile = cmd.get<string>("yFlowFile");//string imgFile = cmd.get<string>("imgFile");//int bound = cmd.get<int>("bound");string vidFile = "vidFile";string xFlowFile = "xFlowFile";string yFlowFile = "yFlowFile";string imgFile = "imgFile";int bound = 80;namedWindow("video", 1);namedWindow("imgX", 1);namedWindow("imgY", 1);namedWindow("Demo", 1);//VideoCapture capture(vidFile);VideoCapture capture("Monkey.mp4");if (!capture.isOpened()) {printf("Could not initialize capturing..\n");return -1;}int frame_num = 0;Mat image, prev_image, prev_grey, grey, frame, flow, cflow;while (true) {capture >> frame;if (frame.empty())break;imshow("video", frame);if (frame_num == 0) {image.create(frame.size(), CV_8UC3);grey.create(frame.size(), CV_8UC1);prev_image.create(frame.size(), CV_8UC3);prev_grey.create(frame.size(), CV_8UC1);frame.copyTo(prev_image);cvtColor(prev_image, prev_grey, CV_BGR2GRAY);frame_num++;continue;}frame.copyTo(image);cvtColor(image, grey, CV_BGR2GRAY);// calcOpticalFlowFarneback(prev_grey,grey,flow,0.5, 3, 15, 3, 5, 1.2, 0 );calcOpticalFlowFarneback(prev_grey, grey, flow, 0.702, 5, 10, 2, 7, 1.5, cv::OPTFLOW_FARNEBACK_GAUSSIAN);prev_image.copyTo(cflow);drawOptFlowMap(flow, cflow, 12, 1.5, Scalar(0, 255, 0));imshow("cflow", cflow);Mat flows[2];split(flow, flows);Mat imgX(flows[0].size(), CV_8UC1);Mat imgY(flows[0].size(), CV_8UC1);convertFlowToImage(flows[0], flows[1], imgX, imgY, -bound, bound);//char tmp[20];//sprintf(tmp, "_%04d.jpg", int(frame_num));//imwrite(xFlowFile + tmp, imgX);//imwrite(yFlowFile + tmp, imgY);//imwrite(imgFile + tmp, image);std::swap(prev_grey, grey);std::swap(prev_image, image);frame_num = frame_num + 1;imshow("imgX", imgX);imshow("imgY", imgY);imshow("Demo", image);if (waitKey(1) == 27) break;}waitKey(0);return 0;
}

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【OpenCV】OpenCV3的第二天——光流法(Optial Flow)运动目标检测

Opencv学习笔记（九）光流法

【算法分析】Lucas–Kanade光流算法

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