kalman 滤波 演示与opencv代码
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转载自:http://blog.csdn.net/onezeros/article/details/6318944
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:
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
- kalman 滤波 演示与opencv代码
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