2010-05-12 01:44:00 +08:00
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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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2013-04-11 21:38:33 +08:00
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#include "opencv2/video/tracking_c.h"
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2010-05-12 01:44:00 +08:00
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/*======================= KALMAN FILTER =========================*/
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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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/* Matrices for zero size velocity: */
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/* Dinamic 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 CvBlobTrackPostProcKalman:public CvBlobTrackPostProcOne
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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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float m_ModelNoise;
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float m_DataNoisePos;
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float m_DataNoiseSize;
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public:
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CvBlobTrackPostProcKalman();
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~CvBlobTrackPostProcKalman();
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CvBlob* Process(CvBlob* pBlob);
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void Release();
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virtual void ParamUpdate();
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}; /* class CvBlobTrackPostProcKalman */
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CvBlobTrackPostProcKalman::CvBlobTrackPostProcKalman()
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{
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m_ModelNoise = 1e-6f;
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m_DataNoisePos = 1e-6f;
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m_DataNoiseSize = 1e-1f;
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#if STATE_NUM>6
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m_DataNoiseSize *= (float)pow(20.,2.);
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#else
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m_DataNoiseSize /= (float)pow(20.,2.);
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#endif
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AddParam("ModelNoise",&m_ModelNoise);
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AddParam("DataNoisePos",&m_DataNoisePos);
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AddParam("DataNoiseSize",&m_DataNoiseSize);
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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(m_ModelNoise) );
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cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(m_DataNoisePos) );
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CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) = m_DataNoiseSize;
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CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) = m_DataNoiseSize;
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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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CvBlobTrackPostProcKalman::~CvBlobTrackPostProcKalman()
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{
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cvReleaseKalman(&m_pKalman);
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}
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void CvBlobTrackPostProcKalman::ParamUpdate()
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{
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cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(m_ModelNoise) );
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cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(m_DataNoisePos) );
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CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) = m_DataNoiseSize;
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CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) = m_DataNoiseSize;
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}
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CvBlob* CvBlobTrackPostProcKalman::Process(CvBlob* pBlob)
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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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}
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else
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{ /* Nonfirst call: */
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cvKalmanPredict(m_pKalman,0);
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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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cvMatMulAdd(m_pKalman->measurement_matrix, m_pKalman->state_post, 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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void CvBlobTrackPostProcKalman::Release()
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{
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delete this;
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}
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2012-06-08 01:21:29 +08:00
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static CvBlobTrackPostProcOne* cvCreateModuleBlobTrackPostProcKalmanOne()
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2010-05-12 01:44:00 +08:00
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{
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return (CvBlobTrackPostProcOne*) new CvBlobTrackPostProcKalman;
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}
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CvBlobTrackPostProc* cvCreateModuleBlobTrackPostProcKalman()
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{
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return cvCreateBlobTrackPostProcList(cvCreateModuleBlobTrackPostProcKalmanOne);
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}
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/*======================= KALMAN FILTER =========================*/
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/*======================= KALMAN PREDICTOR =========================*/
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class CvBlobTrackPredictKalman:public CvBlobTrackPredictor
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{
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private:
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CvBlob m_BlobPredict;
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CvKalman* m_pKalman;
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int m_Frame;
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float m_ModelNoise;
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float m_DataNoisePos;
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float m_DataNoiseSize;
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public:
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CvBlobTrackPredictKalman();
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~CvBlobTrackPredictKalman();
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CvBlob* Predict();
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void Update(CvBlob* pBlob);
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virtual void ParamUpdate();
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void Release()
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{
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delete this;
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}
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}; /* class CvBlobTrackPredictKalman */
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void CvBlobTrackPredictKalman::ParamUpdate()
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{
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cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(m_ModelNoise) );
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cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(m_DataNoisePos) );
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CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) = m_DataNoiseSize;
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CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) = m_DataNoiseSize;
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}
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CvBlobTrackPredictKalman::CvBlobTrackPredictKalman()
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{
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m_ModelNoise = 1e-6f;
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m_DataNoisePos = 1e-6f;
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m_DataNoiseSize = 1e-1f;
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#if STATE_NUM>6
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m_DataNoiseSize *= (float)pow(20.,2.);
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#else
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m_DataNoiseSize /= (float)pow(20.,2.);
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#endif
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AddParam("ModelNoise",&m_ModelNoise);
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AddParam("DataNoisePos",&m_DataNoisePos);
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AddParam("DataNoiseSize",&m_DataNoiseSize);
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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(m_ModelNoise) );
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cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(m_DataNoisePos) );
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CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) = m_DataNoiseSize;
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CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) = m_DataNoiseSize;
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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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CvBlobTrackPredictKalman::~CvBlobTrackPredictKalman()
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{
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cvReleaseKalman(&m_pKalman);
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}
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CvBlob* CvBlobTrackPredictKalman::Predict()
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{
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if(m_Frame >= 2)
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{
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cvKalmanPredict(m_pKalman,0);
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m_BlobPredict.x = m_pKalman->state_pre->data.fl[0];
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m_BlobPredict.y = m_pKalman->state_pre->data.fl[1];
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m_BlobPredict.w = m_pKalman->state_pre->data.fl[2];
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m_BlobPredict.h = m_pKalman->state_pre->data.fl[3];
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}
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return &m_BlobPredict;
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}
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void CvBlobTrackPredictKalman::Update(CvBlob* pBlob)
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{
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float Z[4];
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CvMat Zmat = cvMat(4,1,CV_32F,Z);
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m_BlobPredict = 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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}
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else
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{ /* Nonfirst 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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}
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cvKalmanPredict(m_pKalman,0);
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m_Frame++;
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} /* Update. */
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CvBlobTrackPredictor* cvCreateModuleBlobTrackPredictKalman()
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{
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return (CvBlobTrackPredictor*) new CvBlobTrackPredictKalman;
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}
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/*======================= KALMAN PREDICTOR =========================*/
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