几天来，我一直在研究卡尔曼滤波器的操作，以提高我的人脸检测程序的性能。根据我收集的信息，我整理了一个代码。卡尔曼滤波器部分的代码如下。
int Kalman(int X,int faceWidth,int Y,int faceHeight, IplImage *img1){
CvRandState rng;
const float T = 0.1;
// Initialize Kalman filter object, window, number generator, etc
cvRandInit( &rng, 0, 1, -1, CV_RAND_UNI );
//IplImage* img = cvCreateImage( cvSize(500,500), 8, 3 );
CvKalman* kalman = cvCreateKalman( 4, 4, 0 );
// Initializing with random guesses
// state x_k
CvMat* state = cvCreateMat( 4, 1, CV_32FC1 );
cvRandSetRange( &rng, 0, 0.1, 0 );
rng.disttype = CV_RAND_NORMAL;
cvRand( &rng, state );
// Process noise w_k
CvMat* process_noise = cvCreateMat( 4, 1, CV_32FC1 );
// Measurement z_k
CvMat* measurement = cvCreateMat( 4, 1, CV_32FC1 );
cvZero(measurement);
/* create matrix data */
const float A[] = {
1, 0, T, 0,
0, 1, 0, T,
0, 0, 1, 0,
0, 0, 0, 1
};
const float H[] = {
1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 1, 0,
0, 0, 0, 0
};
//Didn't use this matrix in the end as it gave an error:'ambiguous call to overloaded function'
/* const float P[] = {
pow(320,2), pow(320,2)/T, 0, 0,
pow(320,2)/T, pow(320,2)/pow(T,2), 0, 0,
0, 0, pow(240,2), pow(240,2)/T,
0, 0, pow(240,2)/T, pow(240,2)/pow(T,2)
}; */
const float Q[] = {
pow(T,3)/3, pow(T,2)/2, 0, 0,
pow(T,2)/2, T, 0, 0,
0, 0, pow(T,3)/3, pow(T,2)/2,
0, 0, pow(T,2)/2, T
};
const float R[] = {
1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 1, 0,
0, 0, 0, 0
};
//Copy created matrices into kalman structure
memcpy( kalman->transition_matrix->data.fl, A, sizeof(A));
memcpy( kalman->measurement_matrix->data.fl, H, sizeof(H));
memcpy( kalman->process_noise_cov->data.fl, Q, sizeof(Q));
//memcpy( kalman->error_cov_post->data.fl, P, sizeof(P));
memcpy( kalman->measurement_noise_cov->data.fl, R, sizeof(R));
//Initialize other Kalman Filter parameters
//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(1e-5) );
/* choose initial state */
kalman->state_post->data.fl[0]=X;
kalman->state_post->data.fl[1]=faceWidth;
kalman->state_post->data.fl[2]=Y;
kalman->state_post->data.fl[3]=faceHeight;
//cvRand( &rng, kalman->state_post );
/* predict position of point */
const CvMat* prediction=cvKalmanPredict(kalman,0);
//generate measurement (z_k)
cvRandSetRange( &rng, 0, sqrt(kalman->measurement_noise_cov->data.fl[0]), 0 );
cvRand( &rng, measurement );
cvMatMulAdd( kalman->measurement_matrix, state, measurement, measurement );
//Draw rectangles in detected face location
cvRectangle( img1,
cvPoint( kalman->state_post->data.fl[0], kalman->state_post->data.fl[2] ),
cvPoint( kalman->state_post->data.fl[1], kalman->state_post->data.fl[3] ),
CV_RGB( 0, 255, 0 ), 1, 8, 0 );
cvRectangle( img1,
cvPoint( prediction->data.fl[0], prediction->data.fl[2] ),
cvPoint( prediction->data.fl[1], prediction->data.fl[3] ),
CV_RGB( 0, 0, 255 ), 1, 8, 0 );
cvShowImage("Kalman",img1);
//adjust kalman filter state
cvKalmanCorrect(kalman,measurement);
cvMatMulAdd(kalman->transition_matrix, state, process_noise, state);
return 0;
}
在人脸检测部分（未示出）中，为检测到的人脸画了一个框。'X, Y, faceWidth and faceHeight' 是盒子的坐标以及传入卡尔曼滤波器的宽度和高度。“img1”是视频的当前帧。
结果：
虽然我确实从“state_post”和“prediction”数据中得到了两个新矩形（如代码中所示），但它们似乎都没有比没有卡尔曼滤波器绘制的初始框更稳定。
以下是我的问题：
- 矩阵是否已初始化（转换矩阵 A、测量矩阵 H 等），对于这四个输入情况是否正确？（例如，四个输入的 4*4 矩阵？）
- 我们不能将每个矩阵都设置为单位矩阵吗？
- 我在绘制矩形之前遵循的方法理论上是否正确？我遵循了本文中的示例以及不使用外部输入的“Learning OpenCV”一书。
任何有关这方面的帮助将不胜感激！