mean shift

Mean Shift，我们翻译为“均值飘移”。

其在聚类，图像平滑。

图像分割和跟踪方面得到了比较广泛的应用。

由于本人目前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有一个比较全面的介绍。

（以下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估计（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,如果我们说Mean Shift算法,一般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.然而在以后的很长一段时间内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对基本的Mean Shift算法在以下两个方面做了推广,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,并给出了具体的例子。

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,使得跟踪可以实时的进行。

目前，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，目前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。

下面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讲解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，算法采用的是特征值的加权概率分布来描述目标模型。

这应该是模式识别中主要描述目标的模型，不同于自动控制理论中采用的状态方程。

目标模型共m个特征值（可以理解为像素灰度值）其中X0是窗口中心点向量值（可能为RBG 向量或者灰度值），Xi 是窗口内第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，对应于图像处理可以理解为灰度等级划分的个数，从而特征值u 为对应的灰度等级。

d 函数为脉冲函数，保证只有具有u 特征值的像素才对概率分布作出贡献。

从而k函数可以理解为u 灰度值的一个加权频数。

2、匹配对象，也采用特征值加权概率分布其中，Y 为匹配对象的中心，Xi 是匹配窗口内第i 点向量值，Hh 为匹配窗口的核函数带宽向量。

Ch 为匹配窗口特征向量的归一化常数。

3、匹配对象与目标模型的相似程度，相似函数可采用Bhattacharyya 函数4、匹配过程就是寻找相似函数最大值的寻优过程，Mean-Shift 采用的是梯度下降法。

首先将(Y) 在（Y0）附近进行泰勒级数展开，取前两项。

即：要使得(Y) 向最大值迭代，只要Y 的搜索方向与梯度方向一致即可，通过求导可得到Y0的梯度方向为：为权值。

因此如果如下确定Y1，那么Y1-Y0将与梯度方向一致。

以上为mean shift的数学原理。

有关文字的叙述已经在上一篇中提到了。

用mean shift来跟踪属于确定性算法，粒子滤波器属于统计学方法。

meanshift跟踪算法相对于粒子滤波器来说可能实时性更好一些，但是跟踪的准确性在理论上还是略逊于粒子滤波器的。

mean shift 跟踪的的实质就是通过对应的模板来确定目标的下一个位置。

通过迭代找到新的中心点（即是目标的新的位置点）。

有关跟踪的code如下所示：/**********************************************************************Bilkent University:Mean-shift Tracker based Moving Object Tracker in VideoVersion: 1.0Compiler: Microsoft Visual C++ 6.0 (tested in both debug and releasemode)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_ITARA TION_NO 5#define DISTANCE_ITARA TION_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:计算图像上对应区域的新直方图;新直方图与目标直方图比较,计算权重;根据权重,计算图像上对应区域的形心/质心;根据形心,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.如果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 colorULONG_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);}//初始化一个目标的参数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//如果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 histogramhistogram[i] = 0.0;//for all the pixels in the regionfor(y=max(m_sTrackingObjectTable[m_cActiveObject].Y-m_sTrackingObjectTable[m_cActiveObje ct].H/2,0);y<=min(m_sTrackingObjectTable[m_cActiveObject].Y+m_sTrackingObjectTable[m_c ActiveObject].H/2,m_nImageHeight-1);y++)for(x=max(m_sTrackingObjectTable[m_cActiveObject].X-m_sTrackingObjectTable[m_cActiveObje ct].W/2,0);x<=min(m_sTrackingObjectTable[m_cActiveObject].X+m_sTrackingObjectTable[m_c ActiveObject].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 componentqG = (UBYTE8)pixval8c( g, y, x )/16;//quantize G componentqB = (UBYTE8)pixval8c( b, y, x )/16;//quantize B componenthistogram[4096*E+256*qR+16*qG+qB] += 1; //根据边缘信息, 累计直方图//HISTOGRAM_LENGTH=8192numberOfPixel++;}for (i=0;i<HISTOGRAM_LENGTH;i++) //normalizehistogram[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 boundsx_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)elseweights[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_c ActiveObject].H/2,m_nImageHeight-1);y++)for(x=max(m_sTrackingObjectTable[m_cActiveObject].X-m_sTrackingObjectTable[m_cActiveObje ct].W/2,0);x<=min(m_sTrackingObjectTable[m_cActiveObject].X+m_sTrackingObjectTable[m_c ActiveObject].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 locationm_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 measurementFLOAT32 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;elsedistance -= 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 histogramvoid 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-scalingFindNextFixScale(frame);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+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 pointFindWightsAndCOM(frame,currentHistogram);//derive weights and new location//FindHistogram(frame,currentHistogram); //uptade histogram//UpdateInitialHistogram(currentHistogram);//uptade initial histogramif( ((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);}。