kalman 滤波 演示与opencv代码
在机器视觉中追踪时常会用到预测算法，kalman是你一定知道的。它可以用来预测各种状态，比如说位置，速度等。关于它的理论有很多很好的文献可以参考。opencv给出了kalman filter的一个实现，而且有范例，但估计不少人对它的使用并不清楚，因为我也是其中一个。本文的应用是对二维坐标进行预测和平滑
使用方法：
1、初始化
const int stateNum=4;//状态数，包括（x，y，dx，dy）坐标及速度（每次移动的距离）
const int measureNum=2;//观测量，能看到的是坐标值，当然也可以自己计算速度，但没必要
Kalman* kalman = cvCreateKalman( stateNum, measureNum, 0 );//state(x,y,detaX,detaY)
转移矩阵或者说增益矩阵的值好像有点莫名其妙
float A[stateNum][stateNum] ={//transition matrix 1,0,1,0, 0,1,0,1, 0,0,1,0, 0,0,0,1 };
看下图就清楚了
X1=X+dx，依次类推
所以这个矩阵还是很容易却确定的，可以根据自己的实际情况定制转移矩阵
同样的方法，三维坐标的转移矩阵可以如下
float A[stateNum][stateNum] ={//transition matrix 1,0,0,1,0,0, 0,1,0,0,1,0, 0,0,1,0,0,1, 0,0,0,1,0,0, 0,0,0,0,1,0, 0,0,0,0,0,1 };
当然并不一定得是1和0
2.预测cvKalmanPredict，然后读出自己需要的值
3.更新观测矩阵
4.更新CvKalman
只有第一步麻烦些。上述这几步跟代码中的序号对应
如果你在做tracking，下面的例子或许更有用些。
#include <cv.h> #include <cxcore.h> #include <highgui.h> #include <cmath> #include <vector> #include <iostream> using namespace std; const int winHeight=600; const int winWidth=800; CvPoint mousePosition=cvPoint(winWidth>>1,winHeight>>1); //mouse event callback void mouseEvent(int event, int x, int y, int flags, void *param ) { if (event==CV_EVENT_MOUSEMOVE) { mousePosition=cvPoint(x,y); } } int main (void) { //1.kalman filter setup const int stateNum=4; const int measureNum=2; CvKalman* kalman = cvCreateKalman( stateNum, measureNum, 0 );//state(x,y,detaX,detaY) CvMat* process_noise = cvCreateMat( stateNum, 1, CV_32FC1 ); CvMat* measurement = cvCreateMat( measureNum, 1, CV_32FC1 );//measurement(x,y) CvRNG rng = cvRNG(-1); float A[stateNum][stateNum] ={//transition matrix 1,0,1,0, 0,1,0,1, 0,0,1,0, 0,0,0,1 }; memcpy( kalman->transition_matrix->data.fl,A,sizeof(A)); cvSetIdentity(kalman->measurement_matrix,cvRealScalar(1) ); cvSetIdentity(kalman->process_noise_cov,cvRealScalar(1e-5)); cvSetIdentity(kalman->measurement_noise_cov,cvRealScalar(1e-1)); cvSetIdentity(kalman->error_cov_post,cvRealScalar(1)); //initialize post state of kalman filter at random cvRandArr(&rng,kalman->state_post,CV_RAND_UNI,cvRealScalar(0),cvRealScalar(winHeight>winWidth?winWidth:winHeight)); CvFont font; cvInitFont(&font,CV_FONT_HERSHEY_SCRIPT_COMPLEX,1,1); cvNamedWindow("kalman"); cvSetMouseCallback("kalman",mouseEvent); IplImage* img=cvCreateImage(cvSize(winWidth,winHeight),8,3); while (1){ //2.kalman prediction const CvMat* prediction=cvKalmanPredict(kalman,0); CvPoint predict_pt=cvPoint((int)prediction->data.fl[0],(int)prediction->data.fl[1]); //3.update measurement measurement->data.fl[0]=(float)mousePosition.x; measurement->data.fl[1]=(float)mousePosition.y; //4.update cvKalmanCorrect( kalman, measurement ); //draw cvSet(img,cvScalar(255,255,255,0)); cvCircle(img,predict_pt,5,CV_RGB(0,255,0),3);//predicted point with green cvCircle(img,mousePosition,5,CV_RGB(255,0,0),3);//current position with red char buf[256]; sprintf_s(buf,256,"predicted position:(%3d,%3d)",predict_pt.x,predict_pt.y); cvPutText(img,buf,cvPoint(10,30),&font,CV_RGB(0,0,0)); sprintf_s(buf,256,"current position :(%3d,%3d)",mousePosition.x,mousePosition.y); cvPutText(img,buf,cvPoint(10,60),&font,CV_RGB(0,0,0)); cvShowImage("kalman", img); int key=cvWaitKey(3); if (key==27){//esc break; } } cvReleaseImage(&img); cvReleaseKalman(&kalman); return 0; }
kalman filter 视频演示：
http://v.youku.com/v_show/id_XMjU4MzEyODky.html
demo snapshot：
