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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp" |
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/*======================= KALMAN FILTER AS TRACKER =========================*/ |
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/* State vector is (x,y,w,h,dx,dy,dw,dh). */ |
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/* Measurement is (x,y,w,h) */ |
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/* Dynamic matrix A: */ |
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const float A8[] = { 1, 0, 0, 0, 1, 0, 0, 0, |
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0, 1, 0, 0, 0, 1, 0, 0, |
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0, 0, 1, 0, 0, 0, 1, 0, |
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0, 0, 0, 1, 0, 0, 0, 1, |
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0, 0, 0, 0, 1, 0, 0, 0, |
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0, 0, 0, 0, 0, 1, 0, 0, |
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0, 0, 0, 0, 0, 0, 1, 0, |
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0, 0, 0, 0, 0, 0, 0, 1}; |
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/* Measurement matrix H: */ |
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const float H8[] = { 1, 0, 0, 0, 0, 0, 0, 0, |
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0, 1, 0, 0, 0, 0, 0, 0, |
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0, 0, 1, 0, 0, 0, 0, 0, |
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0, 0, 0, 1, 0, 0, 0, 0}; |
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/* Matices for zero size velocity: */ |
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/* Dynamic matrix A: */ |
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const float A6[] = { 1, 0, 0, 0, 1, 0, |
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0, 1, 0, 0, 0, 1, |
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0, 0, 1, 0, 0, 0, |
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0, 0, 0, 1, 0, 0, |
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0, 0, 0, 0, 1, 0, |
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0, 0, 0, 0, 0, 1}; |
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/* Measurement matrix H: */ |
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const float H6[] = { 1, 0, 0, 0, 0, 0, |
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0, 1, 0, 0, 0, 0, |
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0, 0, 1, 0, 0, 0, |
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0, 0, 0, 1, 0, 0}; |
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#define STATE_NUM 6 |
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#define A A6 |
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#define H H6 |
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class CvBlobTrackerOneKalman:public CvBlobTrackerOne |
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{ |
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private: |
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CvBlob m_Blob; |
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CvKalman* m_pKalman; |
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int m_Frame; |
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public: |
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CvBlobTrackerOneKalman() |
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{ |
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m_Frame = 0; |
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m_pKalman = cvCreateKalman(STATE_NUM,4); |
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memcpy( m_pKalman->transition_matrix->data.fl, A, sizeof(A)); |
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memcpy( m_pKalman->measurement_matrix->data.fl, H, sizeof(H)); |
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cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(1e-5) ); |
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cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(1e-1) ); |
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// CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) *= (float)pow(20,2);
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// CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) *= (float)pow(20,2);
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cvSetIdentity( m_pKalman->error_cov_post, cvRealScalar(1)); |
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cvZero(m_pKalman->state_post); |
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cvZero(m_pKalman->state_pre); |
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SetModuleName("Kalman"); |
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} |
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~CvBlobTrackerOneKalman() |
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{ |
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cvReleaseKalman(&m_pKalman); |
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} |
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virtual void Init(CvBlob* pBlob, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL) |
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{ |
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m_Blob = pBlob[0]; |
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m_pKalman->state_post->data.fl[0] = CV_BLOB_X(pBlob); |
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m_pKalman->state_post->data.fl[1] = CV_BLOB_Y(pBlob); |
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m_pKalman->state_post->data.fl[2] = CV_BLOB_WX(pBlob); |
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m_pKalman->state_post->data.fl[3] = CV_BLOB_WY(pBlob); |
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} |
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virtual CvBlob* Process(CvBlob* pBlob, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL) |
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{ |
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CvBlob* pBlobRes = &m_Blob; |
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float Z[4]; |
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CvMat Zmat = cvMat(4,1,CV_32F,Z); |
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m_Blob = pBlob[0]; |
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if(m_Frame < 2) |
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{ /* First call: */ |
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m_pKalman->state_post->data.fl[0+4] = CV_BLOB_X(pBlob)-m_pKalman->state_post->data.fl[0]; |
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m_pKalman->state_post->data.fl[1+4] = CV_BLOB_Y(pBlob)-m_pKalman->state_post->data.fl[1]; |
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if(m_pKalman->DP>6) |
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{ |
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m_pKalman->state_post->data.fl[2+4] = CV_BLOB_WX(pBlob)-m_pKalman->state_post->data.fl[2]; |
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m_pKalman->state_post->data.fl[3+4] = CV_BLOB_WY(pBlob)-m_pKalman->state_post->data.fl[3]; |
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} |
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m_pKalman->state_post->data.fl[0] = CV_BLOB_X(pBlob); |
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m_pKalman->state_post->data.fl[1] = CV_BLOB_Y(pBlob); |
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m_pKalman->state_post->data.fl[2] = CV_BLOB_WX(pBlob); |
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m_pKalman->state_post->data.fl[3] = CV_BLOB_WY(pBlob); |
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memcpy(m_pKalman->state_pre->data.fl,m_pKalman->state_post->data.fl,sizeof(float)*STATE_NUM); |
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} |
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else |
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{ /* Another call: */ |
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Z[0] = CV_BLOB_X(pBlob); |
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Z[1] = CV_BLOB_Y(pBlob); |
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Z[2] = CV_BLOB_WX(pBlob); |
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Z[3] = CV_BLOB_WY(pBlob); |
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cvKalmanCorrect(m_pKalman,&Zmat); |
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cvKalmanPredict(m_pKalman,0); |
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cvMatMulAdd(m_pKalman->measurement_matrix, m_pKalman->state_pre, NULL, &Zmat); |
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CV_BLOB_X(pBlobRes) = Z[0]; |
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CV_BLOB_Y(pBlobRes) = Z[1]; |
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CV_BLOB_WX(pBlobRes) = Z[2]; |
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CV_BLOB_WY(pBlobRes) = Z[3]; |
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} |
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m_Frame++; |
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return pBlobRes; |
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} |
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virtual void Release() |
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{ |
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delete this; |
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} |
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}; /* class CvBlobTrackerOneKalman */ |
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#if 0 |
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static CvBlobTrackerOne* cvCreateModuleBlobTrackerOneKalman() |
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{ |
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return (CvBlobTrackerOne*) new CvBlobTrackerOneKalman; |
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} |
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CvBlobTracker* cvCreateBlobTrackerKalman() |
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{ |
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return cvCreateBlobTrackerList(cvCreateModuleBlobTrackerOneKalman); |
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} |
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#endif |
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