Mean Shift，我们 翻译为“均值飘移”。其在聚类，图像平滑。图像切割和跟踪方面得到了比較广泛的应用。因为本人眼下研究跟踪方面的东西，故此主要介绍利用Mean Shift方法进行目标跟踪，从而对MeanShift有一个比較全面的介绍。
（下面某些部分转载常峰学长的“Mean Shift概述”） Mean Shift 这个概念最早是由Fukunaga等人于1975年在一篇关于概率密度梯度函数的预计（The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition ）中提出来的,其最初含义正如其名,就是偏移的均值向量,在这里Mean Shift是一个名词,它指代的是一个向量,但随着Mean Shift理论的发展,Mean Shift的含义也发生了变化,假设我们说Mean Shift算法,通常是指一个迭代的步骤,即先算出当前点的偏移均值,移动该点到其偏移均值,然后以此为新的起始点,继续移动,直到满足一定的条件结束.
然而在以后的非常长一段时间内Mean Shift并没有引起人们的注意,直到20年以后,也就是1995年,另外一篇关于Mean Shift的重要文献（Mean shift, mode seeking, and clustering ）才发表.在这篇重要的文献中,Yizong Cheng对主要的Mean Shift算法在下面两个方面做了推广,首先Yizong Cheng定义了一族核函数,使得随着样本与被偏移点的距离不同,其偏移量对均值偏移向量的贡献也不同,其次Yizong Cheng还设定了一个权重系数,使得不同的样本点重要性不一样,这大大扩大了Mean Shift的适用范围.另外Yizong Cheng指出了Mean Shift可能应用的领域,并给出了详细的样例。
Comaniciu等人在还（Mean-shift Blob Tracking through Scale Space）中把非刚体的跟踪问题近似为一个Mean Shift最优化问题,使得跟踪能够实时的进行。眼下，利用Mean Shift进行跟踪已经相当成熟。
目标跟踪不是一个新的问题，眼下在计算机视觉领域内有不少人在研究。所谓跟踪，就是通过已知的图像帧中的目标位置找到目标在下一帧中的位置。
以下主要以代码形式展现Mean Shift在跟踪中的应用。
void CObjectTracker::ObjeckTrackerHandlerByUser(IplImage *frame)//跟踪函数 { m_cActiveObject = 0;
if (m_sTrackingObjectTable[m_cActiveObject].Status) { if (!m_sTrackingObjectTable[m_cActiveObject].assignedAnObject) { FindHistogram(frame,m_sTrackingObjectTable[m_cActiveObject].initHistogram); m_sTrackingObjectTable[m_cActiveObject].assignedAnObject = true; } else { FindNextLocation(frame);//利用mean shift 迭代找出目标下一个位置点
DrawObjectBox(frame); } }
}
void CObjectTracker::FindNextLocation(IplImage *frame){ int i, j, opti, optj;SINT16 scale[3]={-3, 3, 0};FLOAT32 dist, optdist;SINT16 h, w, optX, optY;
//try no-scalingFindNextFixScale(frame);//找出目标的下一个大致范围optdist=LastDist;optX=m_sTrackingObjectTable[m_cActiveObject].X;optY=m_sTrackingObjectTable[m_cActiveObject].Y;
//try one of the 9 possible scalingi=rand()*2/RAND_MAX;j=rand()*2/RAND_MAX;h=m_sTrackingObjectTable[m_cActiveObject].H;w=m_sTrackingObjectTable[m_cActiveObject].W;if(h+scale[i]>10 && w+scale[j]>10 && h+scale[i]<m_nImageHeight/2 && w+scale[j]<m_nImageWidth/2){ m_sTrackingObjectTable[m_cActiveObject].H=h+scale[i]; m_sTrackingObjectTable[m_cActiveObject].W=w+scale[j]; FindNextFixScale(frame); if( (dist=LastDist) < optdist ) //scaling is better { optdist=dist;// printf("Next%f->/n", dist); } else //no scaling is better { m_sTrackingObjectTable[m_cActiveObject].X=optX; m_sTrackingObjectTable[m_cActiveObject].Y=optY; m_sTrackingObjectTable[m_cActiveObject].H=h; m_sTrackingObjectTable[m_cActiveObject].W=w; }};TotalDist+=optdist; //the latest distance// printf("/n");}
这里仍然在跟踪的基础上解说mean shift。首先还是把mean shift的原理用数学公式说一下吧。1、目标模型，算法採用的是特征值的加权概率分布来描写叙述目标模型。这应该是模式识别中主要描写叙述目标的模型，不同于自己主动控制理论中採用的状态方程。目标模型共m个特征值（能够理解为像素灰度值）
当中X0是窗体中心点向量值（可能为RBG 向量或者灰度值）， Xi 是窗体内第i 点向量值。C 为归一化常数，保障q1+q2+q3+……qm=1，H 为核函数的带宽向量。M 为特征值的个数，相应于图像处理能够理解为灰度等级划分的个数，从而特征值u 为相应的灰度等级。d 函数为脉冲函数，保证仅仅有具有u 特征值的像素才对概率分布作出贡献。从而k函数能够理解为u 灰度值的一个加权频数。
2、 匹配对象，也採用特征值加权概率分布
3、 匹配对象与目标模型的类似程度，类似函数可採用Bhattacharyya 函数
4、 匹配过程就是寻找类似函数最大值的寻优过程，Mean-Shift 採用的是梯度下降法。首先将
(Y) 在
(Y) 向最大值迭代，仅仅要Y 的搜索方向与梯度方向一致就可以，通过求导可得到Y0的梯度方向为： 
为权值。因此假设例如以下确定Y1，那么Y1-Y0将与梯度方向一致。 
/**********************************************************************
Bilkent University:
Mean-shift Tracker based Moving Object Tracker in Video
Version: 1.0
Compiler: Microsoft Visual C++ 6.0 (tested in both debug and release
mode)Modified by Mr Zhou
**********************************************************************/
#include "ObjectTracker.h"#include "utils.h"#include <math.h>#include <stdio.h>#include <stdlib.h>/*#define GetRValue(rgb) ((UBYTE8) (rgb)) #define GetGValue(rgb) ((UBYTE8) (((ULONG_32) (rgb)) >> 8)) #define GetBValue(rgb) ((UBYTE8) ((rgb) >> 16)) *///#define RGB(r, g ,b) ((ULONG_32) (((UBYTE8) (r) | ((UBYTE8) (g) << 8)) | (((ULONG_32) (UBYTE8) (b)) << 16)))#define min(a, b) (((a) < (b)) ? (a) : (b))
#define max(a, b) (((a) > (b)) ? (a) : (b))
#define MEANSHIFT_ITARATION_NO 5#define DISTANCE_ITARATION_NO 1#define ALPHA 1#define EDGE_DETECT_TRESHOLD 32///*1 给定目标的初始位置和尺寸, 计算目标在图像中的直方图;2 输入新图像, 迭代直到收敛: 计算图像上相应区域的新直方图;新直方图与目标直方图比較,计算权重;依据权重,计算图像上相应区域的形心/质心;依据形心,修正目标位置;
直方图分为两部分, 每部分大小4096,
RGB的256*256*256种组合, 缩减为16*16*16=4096种组合. 假设目标区域的点是边缘点, 则计入直方图的后一部分, 否则计入直方图的前一部分. *///
CObjectTracker::CObjectTracker(INT32 imW,INT32 imH,IMAGE_TYPE eImageType)
{ m_nImageWidth = imW;m_nImageHeight = imH;m_eIMAGE_TYPE = eImageType;m_cSkipValue = 0;for (UBYTE8 i=0;i<MAX_OBJECT_TRACK_NUMBER;i++)//初始化各个目标
{ m_sTrackingObjectTable[i].Status = false; for(SINT16 j=0;j<HISTOGRAM_LENGTH;j++) m_sTrackingObjectTable[i].initHistogram[j] = 0;}m_nFrameCtr = 0;
m_uTotalTime = 0;m_nMaxEstimationTime = 0;m_cActiveObject = 0;TotalDist=0.0;LastDist=0.0;switch (eImageType)
{ case MD_RGBA: m_cSkipValue = 4 ;break ; case MD_RGB:m_cSkipValue = 3 ; break ;};};CObjectTracker::~CObjectTracker()
{}
//returns pixel values in format |0|B|G|R| wrt to (x.y)/*ULONG_32 CObjectTracker::GetPixelValues(UBYTE8 *frame,SINT16 x,SINT16 y){ ULONG_32 pixelValues = 0;pixelValues = *(frame+(y*m_nImageWidth+x)*m_cSkipValue+2)|//0BGR
*(frame+(y*m_nImageWidth+x)*m_cSkipValue+1) << 8| *(frame+(y*m_nImageWidth+x)*m_cSkipValue) << 16; return(pixelValues);}*/
//set RGB components wrt to (x.y)
void CObjectTracker::SetPixelValues(IplImage *r,IplImage *g,IplImage *b,ULONG_32 pixelValues,SINT16 x,SINT16 y){ // *(frame+(y*m_nImageWidth+x)*m_cSkipValue+2) = UBYTE8(pixelValues & 0xFF);// *(frame+(y*m_nImageWidth+x)*m_cSkipValue+1) = UBYTE8((pixelValues >> 8) & 0xFF);// *(frame+(y*m_nImageWidth+x)*m_cSkipValue) = UBYTE8((pixelValues >> 16) & 0xFF); //setpix32fsetpix8c(r, y, x, UBYTE8(pixelValues & 0xFF));setpix8c(g, y, x, UBYTE8((pixelValues >> 8) & 0xFF));setpix8c(b, y, x, UBYTE8((pixelValues >> 16) & 0xFF));}// returns box color
ULONG_32 CObjectTracker::GetBoxColor(){ ULONG_32 pixelValues = 0;switch(m_cActiveObject)
{ case 0:pixelValues = RGB(255,0,0);break;case 1:pixelValues = RGB(0,255,0);break;case 2:pixelValues = RGB(0,0,255);break;case 3:pixelValues = RGB(255,255,0);break;case 4:pixelValues = RGB(255,0,255);break;case 5:pixelValues = RGB(0,255,255);break;case 6:pixelValues = RGB(255,255,255);break;case 7:pixelValues = RGB(128,0,128);break;case 8:pixelValues = RGB(128,128,0);break;case 9:pixelValues = RGB(128,128,128);break;case 10:pixelValues = RGB(255,128,0);break;case 11:pixelValues = RGB(0,128,128);break;case 12:pixelValues = RGB(123,50,10);break;case 13:pixelValues = RGB(10,240,126);break;case 14:pixelValues = RGB(0,128,255);break;case 15:pixelValues = RGB(128,200,20);break;default:break;}return(pixelValues);
}//初始化一个目标的參数 void CObjectTracker::ObjectTrackerInitObjectParameters(SINT16 x,SINT16 y,SINT16 Width,SINT16 Height){m_cActiveObject = 0;
m_sTrackingObjectTable[m_cActiveObject].X = x;
m_sTrackingObjectTable[m_cActiveObject].Y = y; m_sTrackingObjectTable[m_cActiveObject].W = Width; m_sTrackingObjectTable[m_cActiveObject].H = Height;m_sTrackingObjectTable[m_cActiveObject].vectorX = 0;
m_sTrackingObjectTable[m_cActiveObject].vectorY = 0; m_sTrackingObjectTable[m_cActiveObject].Status = true; m_sTrackingObjectTable[m_cActiveObject].assignedAnObject = false;}//进行一次跟踪
void CObjectTracker::ObjeckTrackerHandlerByUser(IplImage *frame){ m_cActiveObject = 0;if (m_sTrackingObjectTable[m_cActiveObject].Status)
{ if (!m_sTrackingObjectTable[m_cActiveObject].assignedAnObject) { //计算目标的初始直方图 FindHistogram(frame,m_sTrackingObjectTable[m_cActiveObject].initHistogram); m_sTrackingObjectTable[m_cActiveObject].assignedAnObject = true; } else { //在图像上搜索目标 FindNextLocation(frame);DrawObjectBox(frame);
} }}
//Extracts the histogram of box//frame: 图像//histogram: 直方图//在图像frame中计算当前目标的直方图histogram//直方图分为两部分,每部分大小4096,//RGB的256*256*256种组合,缩减为16*16*16=4096种组合//假设目标区域的点是边缘点,则计入直方图的后一部分,//否则计入直方图的前一部分void CObjectTracker::FindHistogram(IplImage *frame, FLOAT32 (*histogram)){ SINT16 i = 0;SINT16 x = 0;SINT16 y = 0;UBYTE8 E = 0;UBYTE8 qR = 0,qG = 0,qB = 0;// ULONG_32 pixelValues = 0;UINT32 numberOfPixel = 0;IplImage* r, * g, * b;r = cvCreateImage( cvGetSize(frame), frame->depth, 1 );
g = cvCreateImage( cvGetSize(frame), frame->depth, 1 );b = cvCreateImage( cvGetSize(frame), frame->depth, 1 );cvCvtPixToPlane( frame, b, g, r, NULL ); //divide color image into separate planes r, g, b. The exact sequence doesn't matter. for (i=0;i<HISTOGRAM_LENGTH;i++) //reset all histogram histogram[i] = 0.0;//for all the pixels in the region
for (y=max(m_sTrackingObjectTable[m_cActiveObject].Y-m_sTrackingObjectTable[m_cActiveObject].H/2,0);y<=min(m_sTrackingObjectTable[m_cActiveObject].Y+m_sTrackingObjectTable[m_cActiveObject].H/2,m_nImageHeight-1);y++) for (x=max(m_sTrackingObjectTable[m_cActiveObject].X-m_sTrackingObjectTable[m_cActiveObject].W/2,0);x<=min(m_sTrackingObjectTable[m_cActiveObject].X+m_sTrackingObjectTable[m_cActiveObject].W/2,m_nImageWidth-1);x++) { //边缘信息: 当前点与上下左右4点灰度差异是否超过阈值 E = CheckEdgeExistance(r, g, b,x,y);qR = (UBYTE8)pixval8c( r, y, x )/16;//quantize R component
qG = (UBYTE8)pixval8c( g, y, x )/16;//quantize G component qB = (UBYTE8)pixval8c( b, y, x )/16;//quantize B componenthistogram[4096*E+256*qR+16*qG+qB] += 1; //依据边缘信息, 累计直方图//HISTOGRAM_LENGTH=8192
numberOfPixel++;
}
for (i=0;i<HISTOGRAM_LENGTH;i++) //normalize
histogram[i] = histogram[i]/numberOfPixel;//for (i=0;i<HISTOGRAM_LENGTH;i++) // printf("histogram[%d]=%d/n",i,histogram[i]); // printf("numberOfPixel=%d/n",numberOfPixel);cvReleaseImage(&r);cvReleaseImage(&g);cvReleaseImage(&b);}
//Draw box around objectvoid CObjectTracker::DrawObjectBox(IplImage *frame){ SINT16 x_diff = 0;SINT16 x_sum = 0;SINT16 y_diff = 0;SINT16 y_sum = 0;SINT16 x = 0;SINT16 y = 0;ULONG_32 pixelValues = 0;IplImage* r, * g, * b;r = cvCreateImage( cvGetSize(frame), frame->depth, 1 );
g = cvCreateImage( cvGetSize(frame), frame->depth, 1 );b = cvCreateImage( cvGetSize(frame), frame->depth, 1 );cvCvtPixToPlane( frame, b, g, r, NULL );pixelValues = GetBoxColor();
//the x left and right bounds
x_sum = min(m_sTrackingObjectTable[m_cActiveObject].X+m_sTrackingObjectTable[m_cActiveObject].W/2+1,m_nImageWidth-1);//右边界x_diff = max(m_sTrackingObjectTable[m_cActiveObject].X-m_sTrackingObjectTable[m_cActiveObject].W/2,0);//左边界//the y upper and lower boundsy_sum = min(m_sTrackingObjectTable[m_cActiveObject].Y+m_sTrackingObjectTable[m_cActiveObject].H/2+1,m_nImageHeight-1);//下边界y_diff = max(m_sTrackingObjectTable[m_cActiveObject].Y-m_sTrackingObjectTable[m_cActiveObject].H/2,0);//上边界for (y=y_diff;y<=y_sum;y++)
{ SetPixelValues(r, g, b,pixelValues,x_diff,y); SetPixelValues(r, g, b,pixelValues,x_diff+1,y);SetPixelValues(r, g, b,pixelValues,x_sum-1,y);
SetPixelValues(r, g, b,pixelValues,x_sum,y);}for (x=x_diff;x<=x_sum;x++){ SetPixelValues(r, g, b,pixelValues,x,y_diff); SetPixelValues(r, g, b,pixelValues,x,y_diff+1);SetPixelValues(r, g, b,pixelValues,x,y_sum-1);
SetPixelValues(r, g, b,pixelValues,x,y_sum);}cvCvtPlaneToPix(b, g, r, NULL, frame);cvReleaseImage(&r);
cvReleaseImage(&g);cvReleaseImage(&b);}// Computes weights and drives the new location of object in the next frame//frame: 图像//histogram: 直方图//计算权重, 更新目标的坐标void CObjectTracker::FindWightsAndCOM(IplImage *frame, FLOAT32 (*histogram)){ SINT16 i = 0;SINT16 x = 0;SINT16 y = 0;UBYTE8 E = 0;FLOAT32 sumOfWeights = 0;SINT16 ptr = 0;UBYTE8 qR = 0,qG = 0,qB = 0;FLOAT32 newX = 0.0;FLOAT32 newY = 0.0;// ULONG_32 pixelValues = 0;IplImage* r, * g, * b; FLOAT32 *weights = new FLOAT32[HISTOGRAM_LENGTH];for (i=0;i<HISTOGRAM_LENGTH;i++)
{ if (histogram[i] >0.0 ) weights[i] = m_sTrackingObjectTable[m_cActiveObject].initHistogram[i]/histogram[i]; //qu/pu(y0) else weights[i] = 0.0;}r = cvCreateImage( cvGetSize(frame), frame->depth, 1 );
g = cvCreateImage( cvGetSize(frame), frame->depth, 1 );b = cvCreateImage( cvGetSize(frame), frame->depth, 1 );cvCvtPixToPlane( frame, b, g, r, NULL ); //divide color image into separate planes r, g, b. The exact sequence doesn't matter.for (y=max(m_sTrackingObjectTable[m_cActiveObject].Y-m_sTrackingObjectTable[m_cActiveObject].H/2,0);y<=min(m_sTrackingObjectTable[m_cActiveObject].Y+m_sTrackingObjectTable[m_cActiveObject].H/2,m_nImageHeight-1);y++)
for (x=max(m_sTrackingObjectTable[m_cActiveObject].X-m_sTrackingObjectTable[m_cActiveObject].W/2,0);x<=min(m_sTrackingObjectTable[m_cActiveObject].X+m_sTrackingObjectTable[m_cActiveObject].W/2,m_nImageWidth-1);x++) { E = CheckEdgeExistance(r, g, b,x,y);qR = (UBYTE8)pixval8c( r, y, x )/16;
qG = (UBYTE8)pixval8c( g, y, x )/16; qB = (UBYTE8)pixval8c( b, y, x )/16;ptr = 4096*E+256*qR+16*qG+qB; //some recalculation here. The bin number of (x, y) can be stroed somewhere in fact.
newX += (weights[ptr]*x);
newY += (weights[ptr]*y);sumOfWeights += weights[ptr];
}if (sumOfWeights>0)
{ m_sTrackingObjectTable[m_cActiveObject].X = SINT16((newX/sumOfWeights) + 0.5); //update location m_sTrackingObjectTable[m_cActiveObject].Y = SINT16((newY/sumOfWeights) + 0.5); }cvReleaseImage(&r);
cvReleaseImage(&g);cvReleaseImage(&b); delete[] weights, weights = 0;}// Returns the distance between two histograms.FLOAT32 CObjectTracker::FindDistance(FLOAT32 (*histogram)){ SINT16 i = 0;FLOAT32 distance = 0; for(i=0;i<HISTOGRAM_LENGTH;i++) distance += FLOAT32(sqrt(DOUBLE64(m_sTrackingObjectTable[m_cActiveObject].initHistogram[i] *histogram[i])));return(sqrt(1-distance));
}//An alternative distance measurement FLOAT32 CObjectTracker::CompareHistogram(UBYTE8 (*histogram)){ SINT16 i = 0;FLOAT32 distance = 0.0;FLOAT32 difference = 0.0; for (i=0;i<HISTOGRAM_LENGTH;i++){ difference = FLOAT32(m_sTrackingObjectTable[m_cActiveObject].initHistogram[i] -histogram[i]);if (difference>0)
distance += difference; else distance -= difference;}return(distance);}// Returns the edge insformation of a pixel at (x,y), assume a large jump of value around edge pixelsUBYTE8 CObjectTracker::CheckEdgeExistance(IplImage *r, IplImage *g, IplImage *b, SINT16 _x,SINT16 _y){ UBYTE8 E = 0;SINT16 GrayCenter = 0;SINT16 GrayLeft = 0;SINT16 GrayRight = 0;SINT16 GrayUp = 0;SINT16 GrayDown = 0;// ULONG_32 pixelValues = 0;// pixelValues = GetPixelValues(frame,_x,_y);
GrayCenter = SINT16(3*pixval8c( r, _y, _x )+6*pixval8c( g, _y, _x )+pixval8c( b, _y, _x ));if (_x>0)
{ // pixelValues = GetPixelValues(frame,_x-1,_y); GrayLeft = SINT16(3*pixval8c( r, _y, _x-1 )+6*pixval8c( g, _y, _x-1 )+pixval8c( b, _y, _x-1 ));}if (_x < (m_nImageWidth-1))
{ // pixelValues = GetPixelValues(frame,_x+1,_y); GrayRight = SINT16(3*pixval8c( r, _y, _x+1 )+6*pixval8c( g, _y, _x+1 )+pixval8c( b, _y, _x+1 ));}if (_y>0)
{ // pixelValues = GetPixelValues(frame,_x,_y-1); GrayUp = SINT16(3*pixval8c( r, _y-1, _x )+6*pixval8c( g, _y-1, _x )+pixval8c( b, _y-1, _x ));}if (_y<(m_nImageHeight-1))
{ // pixelValues = GetPixelValues(frame,_x,_y+1);GrayDown = SINT16(3*pixval8c( r, _y+1, _x )+6*pixval8c( g, _y+1, _x )+pixval8c( b, _y+1, _x ));
}if (abs((GrayCenter-GrayLeft)/10)>EDGE_DETECT_TRESHOLD)
E = 1;if (abs((GrayCenter-GrayRight)/10)>EDGE_DETECT_TRESHOLD)
E = 1;if (abs((GrayCenter-GrayUp)/10)>EDGE_DETECT_TRESHOLD)
E = 1;if (abs((GrayCenter-GrayDown)/10)>EDGE_DETECT_TRESHOLD)
E = 1;return(E);
}// Alpha blending: used to update initial histogram by the current histogram void CObjectTracker::UpdateInitialHistogram(UBYTE8 (*histogram)){ SINT16 i = 0;for (i=0; i<HISTOGRAM_LENGTH; i++) m_sTrackingObjectTable[m_cActiveObject].initHistogram[i] = ALPHA*m_sTrackingObjectTable[m_cActiveObject].initHistogram[i] +(1-ALPHA)*histogram[i];}
// Mean-shift iteration //frame: 图像//MeanShift迭代找出中心点void CObjectTracker::FindNextLocation(IplImage *frame){ int i, j, opti, optj;SINT16 scale[3]={-3, 3, 0};FLOAT32 dist, optdist;SINT16 h, w, optX, optY;//try no-scaling
FindNextFixScale(frame);optdist=LastDist;optX=m_sTrackingObjectTable[m_cActiveObject].X;optY=m_sTrackingObjectTable[m_cActiveObject].Y;//try one of the 9 possible scaling
i=rand()*2/RAND_MAX;j=rand()*2/RAND_MAX;h=m_sTrackingObjectTable[m_cActiveObject].H;w=m_sTrackingObjectTable[m_cActiveObject].W;if(h+scale[i]>10 && w+scale[j]>10 && h+scale[i]<m_nImageHeight/2 && w+scale[j]<m_nImageWidth/2){ m_sTrackingObjectTable[m_cActiveObject].H=h+2*scale[i]; m_sTrackingObjectTable[m_cActiveObject].W=w+2*scale[j]; FindNextFixScale(frame); if( (dist=LastDist) < optdist ) //scaling is better { optdist=dist;// printf("Next%f->/n", dist); } else //no scaling is better { m_sTrackingObjectTable[m_cActiveObject].X=optX; m_sTrackingObjectTable[m_cActiveObject].Y=optY; m_sTrackingObjectTable[m_cActiveObject].H=h; m_sTrackingObjectTable[m_cActiveObject].W=w; }};TotalDist+=optdist; //the latest distance// printf("/n");}void CObjectTracker::FindNextFixScale(IplImage *frame)
{ UBYTE8 iteration = 0;SINT16 optX, optY;FLOAT32 *currentHistogram = new FLOAT32[HISTOGRAM_LENGTH];
FLOAT32 dist, optdist=1.0;for (iteration=0; iteration<MEANSHIFT_ITARATION_NO; iteration++)
{ FindHistogram(frame,currentHistogram); //current frame histogram, use the last frame location as starting point FindWightsAndCOM(frame,currentHistogram);//derive weights and new location //FindHistogram(frame,currentHistogram); //uptade histogram //UpdateInitialHistogram(currentHistogram);//uptade initial histogram if( ((dist=FindDistance(currentHistogram)) < optdist) || iteration==0 ) { optdist=dist; optX=m_sTrackingObjectTable[m_cActiveObject].X; optY=m_sTrackingObjectTable[m_cActiveObject].Y;// printf("%f->", dist); } else //bad iteration, then find a better start point for next iteration { m_sTrackingObjectTable[m_cActiveObject].X=(m_sTrackingObjectTable[m_cActiveObject].X+optX)/2; m_sTrackingObjectTable[m_cActiveObject].Y=(m_sTrackingObjectTable[m_cActiveObject].Y+optY)/2; }}//end form_sTrackingObjectTable[m_cActiveObject].X=optX;m_sTrackingObjectTable[m_cActiveObject].Y=optY;LastDist=optdist; //the latest distance// printf("/n");delete[] currentHistogram, currentHistogram = 0;
}float CObjectTracker::GetTotalDist(void){ return(TotalDist);}