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ZivkovicAGMM.cpp
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ZivkovicAGMM.cpp
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/****************************************************************************
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*
******************************************************************************/
/****************************************************************************
*
* ZivkovicAGMM.cpp
*
* Purpose: Implementation of the Gaussian mixture model (GMM) background
* subtraction algorithm developed by Z. Zivkovic.
*
* Author: Donovan Parks, September 2007
*
* This code is based on code by Z. Zivkovic's. I have changed it from a pure
* C implementation to a cleaner (IMHO) C++ implementation. It is based on the
* following papers:
*
* "Improved adaptive Gausian mixture model for background subtraction"
* Z.Zivkovic
* International Conference Pattern Recognition, UK, August, 2004
*
*
* "Efficient Adaptive Density Estimapion per Image Pixel for the
* Task of Background Subtraction"
* Z.Zivkovic, F. van der Heijden
* Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006.
*
* Zivkovic's code can be obtained at: www.zoranz.net
******************************************************************************/
#include "ZivkovicAGMM.hpp"
using namespace Algorithms::BackgroundSubtraction;
ZivkovicAGMM::ZivkovicAGMM()
{
m_modes = NULL;
m_modes_per_pixel = NULL;
}
ZivkovicAGMM::~ZivkovicAGMM()
{
if(m_modes != NULL)
delete[] m_modes;
if(m_modes_per_pixel != NULL)
delete[] m_modes_per_pixel;
}
void ZivkovicAGMM::Initalize(const BgsParams& param)
{
m_params = (ZivkovicParams&)param;
m_num_bands = 3; //always 3 - not implemented for other values!
m_bg_threshold = 0.75f; //1-cf from the paper
m_variance = 36.0f; // variance for the new mode
m_complexity_prior = 0.05f; // complexity reduction prior constant
// GMM for each pixel
m_modes = new GMM[m_params.Size()*m_params.MaxModes()];
// used modes per pixel
m_modes_per_pixel = new unsigned char[m_params.Size()];
m_background = cvCreateImage(cvSize(m_params.Width(), m_params.Height()), IPL_DEPTH_8U, 3);
}
void ZivkovicAGMM::InitModel(const RgbImage& data)
{
for(unsigned int i = 0; i < m_params.Size(); ++i)
{
m_modes_per_pixel[i] = 0;
}
for(unsigned int i = 0; i < m_params.Size()*m_params.MaxModes(); ++i)
{
m_modes[i].weight = 0;
m_modes[i].sigma = 0;
m_modes[i].muR = 0;
m_modes[i].muG = 0;
m_modes[i].muB = 0;
}
}
void ZivkovicAGMM::Update(int frame_num, const RgbImage& data, const BwImage& update_mask)
{
// it doesn't make sense to have conditional updates in the GMM framework
}
void ZivkovicAGMM::SubtractPixel(long posPixel, const RgbPixel& pixel, unsigned char* pModesUsed,
unsigned char& low_threshold, unsigned char& high_threshold)
{
//calculate distances to the modes (+ sort???)
//here we need to go in descending order!!!
long pos;
bool bFitsPDF=0;
bool bBackgroundLow=false;
bool bBackgroundHigh=false;
float fOneMinAlpha = 1-m_params.Alpha();
float prune = -m_params.Alpha()*m_complexity_prior;
int nModes =* pModesUsed;
float totalWeight = 0.0f;
// calculate number of Gaussians to include in the background model
int backgroundGaussians = 0;
double sum = 0.0;
for(int i = 0; i < nModes; ++i)
{
if(sum < m_bg_threshold)
{
backgroundGaussians++;
sum += m_modes[posPixel+i].weight;
}
else
{
break;
}
}
// update all distributions and check for match with current pixel
for (int iModes = 0; iModes < nModes; iModes++)
{
pos=posPixel+iModes;
float weight = m_modes[pos].weight;
//fit not found yet
if (!bFitsPDF)
{
//check if it belongs to some of the modes
//calculate distance
float var = m_modes[pos].sigma;
float muR = m_modes[pos].muR;
float muG = m_modes[pos].muG;
float muB = m_modes[pos].muB;
float dR=muR - pixel(0);
float dG=muG - pixel(1);
float dB=muB - pixel(2);
// calculate the squared distance
float dist = (dR*dR + dG*dG + dB*dB);
if(dist < m_params.HighThreshold()*var && iModes < backgroundGaussians)
bBackgroundHigh = true;
//check fit
if (dist < m_params.LowThreshold()*var)
{
/////
//belongs to the mode
bFitsPDF = true;
// check if this Gaussian is part of the background model
if(iModes < backgroundGaussians)
bBackgroundLow = true;
//update distribution
float k = m_params.Alpha()/weight;
weight = fOneMinAlpha*weight+prune;
weight += m_params.Alpha();
m_modes[pos].weight = weight;
m_modes[pos].muR = muR - k*(dR);
m_modes[pos].muG = muG - k*(dG);
m_modes[pos].muB = muB - k*(dB);
//limit update speed for cov matrice
//not needed
//k=k>20*m_m_params.Alpha()?20*m_m_params.Alpha():k;
//float sigmanew = var + k*((0.33*(dR*dR+dG*dG+dB*dB))-var);
//float sigmanew = var + k*((dR*dR+dG*dG+dB*dB)-var);
//float sigmanew = var + k*((0.33*dist)-var);
float sigmanew = var + k*(dist-var);
//limit the variance
m_modes[pos].sigma = sigmanew < 4 ? 4 : sigmanew > 5*m_variance ? 5*m_variance : sigmanew;
// Sort weights so they are in desending order. Note that only the weight for this
// mode will increase and that the weight for all modes that were previously larger than
// this one have already been modified and will not be modified again. Thus, we just need to
// the correct position of this mode in the already sorted list.
// Zivkovic implementation has been modified for clarity, but the results are equivalent
/*
for (int iLocal = iModes;iLocal>0;iLocal--)
{
long posLocal=posPixel + iLocal;
if (weight < (m_modes[posLocal-1].weight))
{
break;
}
else
{
//swap
GMM temp = m_modes[posLocal];
m_modes[posLocal] = m_modes[posLocal-1];
m_modes[posLocal-1] = temp;
}
}
*/
for (int iLocal = iModes; iLocal > 0; iLocal--)
{
long posLocal = posPixel + iLocal;
if (m_modes[posLocal].weight > m_modes[posLocal-1].weight)
{
//swap
GMM temp = m_modes[posLocal];
m_modes[posLocal] = m_modes[posLocal-1];
m_modes[posLocal-1] = temp;
}
else
{
break;
}
}
}
else
{
weight = fOneMinAlpha*weight+prune;
//check prune
if (weight < -prune)
{
weight=0.0;
nModes--;
}
m_modes[pos].weight = weight;
}
//check if it fits the current mode (2.5 sigma)
///////
}
//fit not found yet
/////
else
{
weight = fOneMinAlpha*weight + prune;
//check prune
if (weight < -prune)
{
weight=0.0;
nModes--;
}
m_modes[pos].weight = weight;
}
totalWeight += weight;
}
//renormalize weights so they sum to 1
for (int iLocal = 0; iLocal < nModes; iLocal++)
{
m_modes[posPixel+ iLocal].weight = m_modes[posPixel+ iLocal].weight/totalWeight;
}
//make new mode if needed and exit
if (!bFitsPDF)
{
if (nModes == m_params.MaxModes())
{
//replace the weakest
}
else
{
nModes++;
}
pos = posPixel + nModes-1;
if (nModes==1)
m_modes[pos].weight=1;
else
m_modes[pos].weight=m_params.Alpha();
// Zivkovic implementation changes as this will not result in the
// weights adding to 1
/*
int iLocal;
for (iLocal = 0; iLocal < m_params.MaxModes()odes-1; iLocal++)
{
m_modes[posPixel+ iLocal].weight *= fOneMinAlpha;
}
*/
// Revised implementation:
//renormalize weights
int iLocal;
float sum = 0.0;
for (iLocal = 0; iLocal < nModes; iLocal++)
{
sum += m_modes[posPixel+ iLocal].weight;
}
float invSum = 1.0f/sum;
for (iLocal = 0; iLocal < nModes; iLocal++)
{
m_modes[posPixel+ iLocal].weight *= invSum;
}
m_modes[pos].muR=pixel(0);
m_modes[pos].muG=pixel(1);
m_modes[pos].muB=pixel(2);
m_modes[pos].sigma=m_variance;
// Zivkovic implementation to sort GMM so they are sorted in descending order according to their weight.
// It has been revised for clarity, but the results are equivalent
/*
for (iLocal = m_params.MaxModes()odes-1; iLocal > 0; iLocal--)
{
long posLocal = posPixel + iLocal;
if (m_params.Alpha() < (m_modes[posLocal-1].weight))
{
break;
}
else
{
//swap
GMM temp = m_modes[posLocal];
m_modes[posLocal] = m_modes[posLocal-1];
m_modes[posLocal-1] = temp;
}
}
*/
// sort GMM so they are sorted in descending order according to their weight
for (iLocal = nModes-1; iLocal > 0; iLocal--)
{
long posLocal = posPixel + iLocal;
if (m_modes[posLocal].weight > m_modes[posLocal-1].weight)
{
//swap
GMM temp = m_modes[posLocal];
m_modes[posLocal] = m_modes[posLocal-1];
m_modes[posLocal-1] = temp;
}
else
{
break;
}
}
}
//set the number of modes
*pModesUsed=nModes;
if(bBackgroundLow)
{
low_threshold = BACKGROUND;
}
else
{
low_threshold = FOREGROUND;
}
if(bBackgroundHigh)
{
high_threshold = BACKGROUND;
}
else
{
high_threshold = FOREGROUND;
}
}
///////////////////////////////////////////////////////////////////////////////
//Input:
// data - a pointer to the data of a RGB image of the same size
//Output:
// output - a pointer to the data of a gray value image of the same size
// (the memory should already be reserved)
// values: 255-foreground, 125-shadow, 0-background
///////////////////////////////////////////////////////////////////////////////
void ZivkovicAGMM::Subtract(int frame_num, const RgbImage& data,
BwImage& low_threshold_mask, BwImage& high_threshold_mask)
{
unsigned char low_threshold, high_threshold;
// update each pixel of the image
long posPixel;
unsigned char* pUsedModes=m_modes_per_pixel;
for(unsigned int r = 0; r < m_params.Height(); ++r)
{
for(unsigned int c = 0; c < m_params.Width(); ++c)
{
//update model+ background subtract
posPixel=(r*m_params.Width()+c)*m_params.MaxModes();
SubtractPixel(posPixel, data(r,c), pUsedModes, low_threshold, high_threshold);
low_threshold_mask(r,c) = low_threshold;
high_threshold_mask(r,c) = high_threshold;
m_background(r,c,0) = (unsigned char)m_modes[posPixel].muR;
m_background(r,c,1) = (unsigned char)m_modes[posPixel].muG;
m_background(r,c,2) = (unsigned char)m_modes[posPixel].muB;
pUsedModes++;
}
}
}