计算机视觉
图像处理

基于粒子滤波器的目标跟踪算法及实现

推荐大家看论文《An adaptive color-based particle filter》

直接截图硕士毕业论文的第二章的一部分,应该讲得比较详细了。最后给出当时在pudn找到的最适合学习的实现代码

代码实现:

运行方式:按P停止,在前景窗口鼠标点击目标,会自动生成外接矩形,再次按P,对该选定目标进行跟踪。

// TwoLevel.cpp : 定义控制台应用程序的入口点。
//

/************************************************************************/
/*参考文献real-time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes  */
/************************************************************************/

#include "stdafx.h"
#include 
#include 
#include 
#include 
# include 
#include 
using namespace std;


#define B(image,x,y) ((uchar*)(image->imageData + image->widthStep*(y)))[(x)*3]		//B
#define G(image,x,y) ((uchar*)(image->imageData + image->widthStep*(y)))[(x)*3+1]	//G
#define R(image,x,y) ((uchar*)(image->imageData + image->widthStep*(y)))[(x)*3+2]	//R
#define S(image,x,y) ((uchar*)(image->imageData + image->widthStep*(y)))[(x)]	
#define  Num 10  //帧差的间隔
#define  T 40    //Tf
#define Re 30     //
#define ai 0.08   //学习率

#define CONTOUR_MAX_AREA 10000
#define CONTOUR_MIN_AREA 50

# define R_BIN      8  /* 红色分量的直方图条数 */
# define G_BIN      8  /* 绿色分量的直方图条数 */
# define B_BIN      8  /* 兰色分量的直方图条数 */ 

# define R_SHIFT    5  /* 与上述直方图条数对应 */
# define G_SHIFT    5  /* 的R、G、B分量左移位数 */
# define B_SHIFT    5  /* log2( 256/8 )为移动位数 */

/*
采用Park and Miller方法产生[0,1]之间均匀分布的伪随机数
算法详细描述见:
[1] NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING.
Cambridge University Press. 1992. pp.278-279.
[2] Park, S.K., and Miller, K.W. 1988, Communications of the ACM, 
vol. 31, pp. 1192–1201.
*/

#define IA 16807
#define IM 2147483647
#define AM (1.0/IM)
#define IQ 127773
#define IR 2836
#define MASK 123459876


typedef struct __SpaceState {  /* 状态空间变量 */
	int xt;               /* x坐标位置 */
	int yt;               /* x坐标位置 */
	float v_xt;           /* x方向运动速度 */
	float v_yt;           /* y方向运动速度 */
	int Hxt;              /* x方向半窗宽 */
	int Hyt;              /* y方向半窗宽 */
	float at_dot;         /* 尺度变换速度 */
} SPACESTATE;


bool pause=false;//是否暂停
bool track = false;//是否跟踪
IplImage *curframe=NULL; 
IplImage *pBackImg=NULL;
IplImage *pFrontImg=NULL;
IplImage *pTrackImg =NULL;
unsigned char * img;//把iplimg改到char*  便于计算
int xin,yin;//跟踪时输入的中心点
int xout,yout;//跟踪时得到的输出中心点
int Wid,Hei;//图像的大小
int WidIn,HeiIn;//输入的半宽与半高
int WidOut,HeiOut;//输出的半宽与半高

long ran_seed = 802163120; /* 随机数种子,为全局变量,设置缺省值 */

float DELTA_T = (float)0.05;    /* 帧频,可以为30,25,15,10等 */
int POSITION_DISTURB = 15;      /* 位置扰动幅度   */
float VELOCITY_DISTURB = 40.0;  /* 速度扰动幅值   */
float SCALE_DISTURB = 0.0;      /* 窗宽高扰动幅度 */
float SCALE_CHANGE_D = (float)0.001;   /* 尺度变换速度扰动幅度 */

int NParticle = 75;       /* 粒子个数   */
float * ModelHist = NULL; /* 模型直方图 */
SPACESTATE * states = NULL;  /* 状态数组 */
float * weights = NULL;   /* 每个粒子的权重 */
int nbin;                 /* 直方图条数 */
float Pi_Thres = (float)0.90; /* 权重阈值   */
float Weight_Thres = (float)0.0001;  /* 最大权重阈值,用来判断是否目标丢失 */


/*
设置种子数
一般利用系统时间来进行设置,也可以直接传入一个long型整数
*/
long set_seed( long setvalue )
{
	if ( setvalue != 0 ) /* 如果传入的参数setvalue!=0,设置该数为种子 */
		ran_seed = setvalue;
	else                 /* 否则,利用系统时间为种子数 */
	{
		ran_seed = time(NULL);
	}
	return( ran_seed );
}

/*
计算一幅图像中某个区域的彩色直方图分布
输入参数:
int x0, y0:           指定图像区域的中心点
int Wx, Hy:           指定图像区域的半宽和半高
unsigned char * image:图像数据,按从左至右,从上至下的顺序扫描,
颜色排列次序:RGB, RGB, ...
(或者:YUV, YUV, ...)
int W, H:             图像的宽和高
输出参数:
float * ColorHist:    彩色直方图,颜色索引按:
i = r * G_BIN * B_BIN + g * B_BIN + b排列
int bins:             彩色直方图的条数R_BIN*G_BIN*B_BIN(这里取8x8x8=512)
*/
void CalcuColorHistogram( int x0, int y0, int Wx, int Hy, 
						 unsigned char * image, int W, int H,
						 float * ColorHist, int bins )
{
	int x_begin, y_begin;  /* 指定图像区域的左上角坐标 */
	int y_end, x_end;
	int x, y, i, index;
	int r, g, b;
	float k, r2, f;
	int a2;

	for ( i = 0; i < bins; i++ )     /* 直方图各个值赋0 */
		ColorHist[i] = 0.0;
	/* 考虑特殊情况:x0, y0在图像外面,或者,Wx<=0, Hy<=0 */
	/* 此时强制令彩色直方图为0 */
	if ( ( x0 < 0 ) || (x0 >= W) || ( y0 < 0 ) || ( y0 >= H ) 
		|| ( Wx <= 0 ) || ( Hy <= 0 ) ) return;

	x_begin = x0 - Wx;               /* 计算实际高宽和区域起始点 */
	y_begin = y0 - Hy;
	if ( x_begin < 0 ) x_begin = 0;
	if ( y_begin < 0 ) y_begin = 0;
	x_end = x0 + Wx;
	y_end = y0 + Hy;
	if ( x_end >= W ) x_end = W-1;
	if ( y_end >= H ) y_end = H-1;
	a2 = Wx*Wx+Hy*Hy;                /* 计算核函数半径平方a^2 */
	f = 0.0;                         /* 归一化系数 */
	for ( y = y_begin; y <= y_end; y++ )
		for ( x = x_begin; x <= x_end; x++ )
		{
			r = image[(y*W+x)*3] >> R_SHIFT;   /* 计算直方图 */
			g = image[(y*W+x)*3+1] >> G_SHIFT; /*移位位数根据R、G、B条数 */
			b = image[(y*W+x)*3+2] >> B_SHIFT;
			index = r * G_BIN * B_BIN + g * B_BIN + b;
			r2 = (float)(((y-y0)*(y-y0)+(x-x0)*(x-x0))*1.0/a2); /* 计算半径平方r^2 */
			k = 1 - r2;   /* 核函数k(r) = 1-r^2, |r| < 1; 其他值 k(r) = 0 */
			f = f + k;
			ColorHist[index] = ColorHist[index] + k;  /* 计算核密度加权彩色直方图 */
		}
		for ( i = 0; i < bins; i++ )     /* 归一化直方图 */
			ColorHist[i] = ColorHist[i]/f;

		return;
}

/*
计算Bhattacharyya系数
输入参数:
float * p, * q:      两个彩色直方图密度估计
int bins:            直方图条数
返回值:
Bhattacharyya系数
*/
float CalcuBhattacharyya( float * p, float * q, int bins )
{
	int i;
	float rho;

	rho = 0.0;
	for ( i = 0; i < bins; i++ )
		rho = (float)(rho + sqrt( p[i]*q[i] ));

	return( rho );
}


/*# define RECIP_SIGMA  3.98942280401  / * 1/(sqrt(2*pi)*sigma), 这里sigma = 0.1 * /*/
# define SIGMA2       0.02           /* 2*sigma^2, 这里sigma = 0.1 */

float CalcuWeightedPi( float rho )
{
	float pi_n, d2;

	d2 = 1 - rho;
	//pi_n = (float)(RECIP_SIGMA * exp( - d2/SIGMA2 ));
	pi_n = (float)(exp( - d2/SIGMA2 ));

	return( pi_n );
}

/*
采用Park and Miller方法产生[0,1]之间均匀分布的伪随机数
算法详细描述见:
[1] NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING.
Cambridge University Press. 1992. pp.278-279.
[2] Park, S.K., and Miller, K.W. 1988, Communications of the ACM, 
vol. 31, pp. 1192–1201.
*/

float ran0(long *idum)
{
	long k;
	float ans;

	/* *idum ^= MASK;*/      /* XORing with MASK allows use of zero and other */
	k=(*idum)/IQ;            /* simple bit patterns for idum.                 */
	*idum=IA*(*idum-k*IQ)-IR*k;  /* Compute idum=(IA*idum) % IM without over- */
	if (*idum < 0) *idum += IM;  /* flows by Schrage’s method.               */
	ans=AM*(*idum);          /* Convert idum to a floating result.            */
	/* *idum ^= MASK;*/      /* Unmask before return.                         */
	return ans;
}


/*
获得一个[0,1]之间均匀分布的随机数
*/
float rand0_1()
{
	return( ran0( &ran_seed ) );
}



/*
获得一个x - N(u,sigma)Gaussian分布的随机数
*/
float randGaussian( float u, float sigma )
{
	float x1, x2, v1, v2;
	float s = 100.0;
	float y;

	/*
	使用筛选法产生正态分布N(0,1)的随机数(Box-Mulles方法)
	1. 产生[0,1]上均匀随机变量X1,X2
	2. 计算V1=2*X1-1,V2=2*X2-1,s=V1^2+V2^2
	3. 若s<=1,转向步骤4,否则转1
	4. 计算A=(-2ln(s)/s)^(1/2),y1=V1*A, y2=V2*A
	y1,y2为N(0,1)随机变量
	*/
	while ( s > 1.0 )
	{
		x1 = rand0_1();
		x2 = rand0_1();
		v1 = 2 * x1 - 1;
		v2 = 2 * x2 - 1;
		s = v1*v1 + v2*v2;
	}
	y = (float)(sqrt( -2.0 * log(s)/s ) * v1);
	/*
	根据公式
	z = sigma * y + u
	将y变量转换成N(u,sigma)分布
	*/
	return( sigma * y + u );	
}



/*
初始化系统
int x0, y0:        初始给定的图像目标区域坐标
int Wx, Hy:        目标的半宽高
unsigned char * img:图像数据,RGB形式
int W, H:          图像宽高
*/
int Initialize( int x0, int y0, int Wx, int Hy,
			   unsigned char * img, int W, int H )
{
	int i, j;
	float rn[7];

	set_seed( 0 ); /* 使用系统时钟作为种子,这个函数在 */
	/* 系统初始化时候要调用一次,且仅调用1次 */
	//NParticle = 75; /* 采样粒子个数 */
	//Pi_Thres = (float)0.90; /* 设置权重阈值 */
	states = new SPACESTATE [NParticle]; /* 申请状态数组的空间 */
	if ( states == NULL ) return( -2 );
	weights = new float [NParticle];     /* 申请粒子权重数组的空间 */
	if ( weights == NULL ) return( -3 );	
	nbin = R_BIN * G_BIN * B_BIN; /* 确定直方图条数 */
	ModelHist = new float [nbin]; /* 申请直方图内存 */
	if ( ModelHist == NULL ) return( -1 );

	/* 计算目标模板直方图 */
	CalcuColorHistogram( x0, y0, Wx, Hy, img, W, H, ModelHist, nbin );

	/* 初始化粒子状态(以(x0,y0,1,1,Wx,Hy,0.1)为中心呈N(0,0.4)正态分布) */
	states[0].xt = x0;
	states[0].yt = y0;
	states[0].v_xt = (float)0.0; // 1.0
	states[0].v_yt = (float)0.0; // 1.0
	states[0].Hxt = Wx;
	states[0].Hyt = Hy;
	states[0].at_dot = (float)0.0; // 0.1
	weights[0] = (float)(1.0/NParticle); /* 0.9; */
	for ( i = 1; i < NParticle; i++ )
	{
		for ( j = 0; j < 7; j++ ) rn[j] = randGaussian( 0, (float)0.6 ); /* 产生7个随机高斯分布的数 */
		states[i].xt = (int)( states[0].xt + rn[0] * Wx );
		states[i].yt = (int)( states[0].yt + rn[1] * Hy );
		states[i].v_xt = (float)( states[0].v_xt + rn[2] * VELOCITY_DISTURB );
		states[i].v_yt = (float)( states[0].v_yt + rn[3] * VELOCITY_DISTURB );
		states[i].Hxt = (int)( states[0].Hxt + rn[4] * SCALE_DISTURB );
		states[i].Hyt = (int)( states[0].Hyt + rn[5] * SCALE_DISTURB );
		states[i].at_dot = (float)( states[0].at_dot + rn[6] * SCALE_CHANGE_D );
		/* 权重统一为1/N,让每个粒子有相等的机会 */
		weights[i] = (float)(1.0/NParticle);
	}

	return( 1 );
}



/*
计算归一化累计概率c'_i
输入参数:
float * weight:    为一个有N个权重(概率)的数组
int N:             数组元素个数
输出参数:
float * cumulateWeight: 为一个有N+1个累计权重的数组,
cumulateWeight[0] = 0;
*/
void NormalizeCumulatedWeight( float * weight, float * cumulateWeight, int N )
{
	int i;

	for ( i = 0; i < N+1; i++ ) 
		cumulateWeight[i] = 0;
	for ( i = 0; i < N; i++ )
		cumulateWeight[i+1] = cumulateWeight[i] + weight[i];
	for ( i = 0; i < N+1; i++ )
		cumulateWeight[i] = cumulateWeight[i]/ cumulateWeight[N];

	return;
}

/*
折半查找,在数组NCumuWeight[N]中寻找一个最小的j,使得
NCumuWeight[j] <=v
float v:              一个给定的随机数
float * NCumuWeight:  权重数组
int N:                数组维数
返回值:
数组下标序号
*/
int BinearySearch( float v, float * NCumuWeight, int N )
{
	int l, r, m;

	l = 0; 	r = N-1;   /* extreme left and extreme right components' indexes */
	while ( r >= l)
	{
		m = (l+r)/2;
		if ( v >= NCumuWeight[m] && v < NCumuWeight[m+1] ) return( m );
		if ( v < NCumuWeight[m] ) r = m - 1;
		else l = m + 1;
	}
	return( 0 );
}

/*
重新进行重要性采样
输入参数:
float * c:          对应样本权重数组pi(n)
int N:              权重数组、重采样索引数组元素个数
输出参数:
int * ResampleIndex:重采样索引数组
*/
void ImportanceSampling( float * c, int * ResampleIndex, int N )
{
	float rnum, * cumulateWeight;
	int i, j;

	cumulateWeight = new float [N+1]; /* 申请累计权重数组内存,大小为N+1 */
	NormalizeCumulatedWeight( c, cumulateWeight, N ); /* 计算累计权重 */
	for ( i = 0; i < N; i++ )
	{
		rnum = rand0_1();       /* 随机产生一个[0,1]间均匀分布的数 */ 
		j = BinearySearch( rnum, cumulateWeight, N+1 ); /* 搜索<=rnum的最小索引j */
		if ( j == N ) j--;
		ResampleIndex[i] = j;	/* 放入重采样索引数组 */		
	}

	delete cumulateWeight;

	return;	
}

/*
样本选择,从N个输入样本中根据权重重新挑选出N个
输入参数:
SPACESTATE * state:     原始样本集合(共N个)
float * weight:         N个原始样本对应的权重
int N:                  样本个数
输出参数:
SPACESTATE * state:     更新过的样本集
*/
void ReSelect( SPACESTATE * state, float * weight, int N )
{
	SPACESTATE * tmpState;
	int i, * rsIdx;

	tmpState = new SPACESTATE[N];
	rsIdx = new int[N];

	ImportanceSampling( weight, rsIdx, N ); /* 根据权重重新采样 */
	for ( i = 0; i < N; i++ )
		tmpState[i] = state[rsIdx[i]];//temState为临时变量,其中state[i]用state[rsIdx[i]]来代替
	for ( i = 0; i < N; i++ )
		state[i] = tmpState[i];

	delete[] tmpState;
	delete[] rsIdx;

	return;
}

/*
传播:根据系统状态方程求取状态预测量
状态方程为: S(t) = A S(t-1) + W(t-1)
W(t-1)为高斯噪声
输入参数:
SPACESTATE * state:      待求的状态量数组
int N:                   待求状态个数
输出参数:
SPACESTATE * state:      更新后的预测状态量数组
*/
void Propagate( SPACESTATE * state, int N)
{
	int i;
	int j;
	float rn[7];

	/* 对每一个状态向量state[i](共N个)进行更新 */
	for ( i = 0; i < N; i++ )  /* 加入均值为0的随机高斯噪声 */
	{
		for ( j = 0; j < 7; j++ ) rn[j] = randGaussian( 0, (float)0.6 ); /* 产生7个随机高斯分布的数 */
		state[i].xt = (int)(state[i].xt + state[i].v_xt * DELTA_T + rn[0] * state[i].Hxt + 0.5);
		state[i].yt = (int)(state[i].yt + state[i].v_yt * DELTA_T + rn[1] * state[i].Hyt + 0.5);
		state[i].v_xt = (float)(state[i].v_xt + rn[2] * VELOCITY_DISTURB);
		state[i].v_yt = (float)(state[i].v_yt + rn[3] * VELOCITY_DISTURB);
		state[i].Hxt = (int)(state[i].Hxt+state[i].Hxt*state[i].at_dot + rn[4] * SCALE_DISTURB + 0.5);
		state[i].Hyt = (int)(state[i].Hyt+state[i].Hyt*state[i].at_dot + rn[5] * SCALE_DISTURB + 0.5);
		state[i].at_dot = (float)(state[i].at_dot + rn[6] * SCALE_CHANGE_D);
		cvCircle(pTrackImg,cvPoint(state[i].xt,state[i].yt),3, CV_RGB(0,255,0),-1);
	}
	return;
}

/*
观测,根据状态集合St中的每一个采样,观测直方图,然后
更新估计量,获得新的权重概率
输入参数:
SPACESTATE * state:      状态量数组
int N:                   状态量数组维数
unsigned char * image:   图像数据,按从左至右,从上至下的顺序扫描,
颜色排列次序:RGB, RGB, ...						 
int W, H:                图像的宽和高
float * ObjectHist:      目标直方图
int hbins:               目标直方图条数
输出参数:
float * weight:          更新后的权重
*/
void Observe( SPACESTATE * state, float * weight, int N,
			 unsigned char * image, int W, int H,
			 float * ObjectHist, int hbins )
{
	int i;
	float * ColorHist;
	float rho;

	ColorHist = new float[hbins];

	for ( i = 0; i < N; i++ )
	{
		/* (1) 计算彩色直方图分布 */
		CalcuColorHistogram( state[i].xt, state[i].yt,state[i].Hxt, state[i].Hyt,
			image, W, H, ColorHist, hbins );
		/* (2) Bhattacharyya系数 */
		rho = CalcuBhattacharyya( ColorHist, ObjectHist, hbins );
		/* (3) 根据计算得的Bhattacharyya系数计算各个权重值 */
		weight[i] = CalcuWeightedPi( rho );		
	}

	delete ColorHist;

	return;	
}

/*
估计,根据权重,估计一个状态量作为跟踪输出
输入参数:
SPACESTATE * state:      状态量数组
float * weight:          对应权重
int N:                   状态量数组维数
输出参数:
SPACESTATE * EstState:   估计出的状态量
*/
void Estimation( SPACESTATE * state, float * weight, int N, 
				SPACESTATE & EstState )
{
	int i;
	float at_dot, Hxt, Hyt, v_xt, v_yt, xt, yt;
	float weight_sum;

	at_dot = 0;
	Hxt = 0; 	Hyt = 0;
	v_xt = 0;	v_yt = 0;
	xt = 0;  	yt = 0;
	weight_sum = 0;
	for ( i = 0; i < N; i++ ) /* 求和 */
	{
		at_dot += state[i].at_dot * weight[i];
		Hxt += state[i].Hxt * weight[i];
		Hyt += state[i].Hyt * weight[i];
		v_xt += state[i].v_xt * weight[i];
		v_yt += state[i].v_yt * weight[i];
		xt += state[i].xt * weight[i];
		yt += state[i].yt * weight[i];
		weight_sum += weight[i];
	}
	/* 求平均 */
	if ( weight_sum <= 0 ) weight_sum = 1; /* 防止被0除,一般不会发生 */
	EstState.at_dot = at_dot/weight_sum;
	EstState.Hxt = (int)(Hxt/weight_sum + 0.5 );
	EstState.Hyt = (int)(Hyt/weight_sum + 0.5 );
	EstState.v_xt = v_xt/weight_sum;
	EstState.v_yt = v_yt/weight_sum;
	EstState.xt = (int)(xt/weight_sum + 0.5 );
	EstState.yt = (int)(yt/weight_sum + 0.5 );

	return;
}


/************************************************************
模型更新
输入参数:
SPACESTATE EstState:   状态量的估计值
float * TargetHist:    目标直方图
int bins:              直方图条数
float PiT:             阈值(权重阈值)
unsigned char * img:   图像数据,RGB形式
int W, H:              图像宽高 
输出:
float * TargetHist:    更新的目标直方图
************************************************************/
# define ALPHA_COEFFICIENT      0.2     /* 目标模型更新权重取0.1-0.3 */

int ModelUpdate( SPACESTATE EstState, float * TargetHist, int bins, float PiT,
				unsigned char * img, int W, int H )
{
	float * EstHist, Bha, Pi_E;
	int i, rvalue = -1;

	EstHist = new float [bins];

	/* (1)在估计值处计算目标直方图 */
	CalcuColorHistogram( EstState.xt, EstState.yt, EstState.Hxt, 
		EstState.Hyt, img, W, H, EstHist, bins );
	/* (2)计算Bhattacharyya系数 */
	Bha  = CalcuBhattacharyya( EstHist, TargetHist, bins );
	/* (3)计算概率权重 */
	Pi_E = CalcuWeightedPi( Bha );

	if ( Pi_E > PiT ) 
	{
		for ( i = 0; i < bins; i++ )
		{
			TargetHist[i] = (float)((1.0 - ALPHA_COEFFICIENT) * TargetHist[i]
			+ ALPHA_COEFFICIENT * EstHist[i]);
		}
		rvalue = 1;
	}

	delete EstHist;

	return( rvalue );
}

/*
系统清除
*/
void ClearAll()
{
	if ( ModelHist != NULL ) delete [] ModelHist;
	if ( states != NULL ) delete [] states;
	if ( weights != NULL ) delete [] weights;

	return;
}

/**********************************************************************
基于彩色直方图的粒子滤波算法总流程
输入参数:
unsigned char * img: 图像数据,RGB形式
int W, H:            图像宽高
输出参数:
int &xc, &yc:        找到的图像目标区域中心坐标
int &Wx_h, &Hy_h:    找到的目标的半宽高 
float &max_weight:   最大权重值
返回值:              
成功1,否则-1

基于彩色直方图的粒子滤波跟踪算法的完整使用方法为:
(1)读取彩色视频中的1帧,并确定初始区域,以此获得该区域的中心点、
目标的半高、宽,和图像数组(RGB形式)、图像高宽参数。
采用初始化函数进行初始化
int Initialize( int x0, int y0, int Wx, int Hy,
unsigned char * img, int W, int H )
(2)循环调用下面函数,直到N帧图像结束
int ColorParticleTracking( unsigned char * image, int W, int H, 
int & xc, int & yc, int & Wx_h, int & Hy_h )
每次调用的输出为:目标中心坐标和目标的半高宽
如果函数返回值<0,则表明目标丢失。
(3)清除系统各个变量,结束跟踪
void ClearAll()

**********************************************************************/
int ColorParticleTracking( unsigned char * image, int W, int H, 
						  int & xc, int & yc, int & Wx_h, int & Hy_h,
						  float & max_weight)
{
	SPACESTATE EState;
	int i;
	/* 选择:选择样本,并进行重采样 */
	ReSelect( states, weights, NParticle );
	/* 传播:采样状态方程,对状态变量进行预测 */
	Propagate( states, NParticle);
	/* 观测:对状态量进行更新 */
	Observe( states, weights, NParticle, image, W, H,
		ModelHist, nbin );
	/* 估计:对状态量进行估计,提取位置量 */
	Estimation( states, weights, NParticle, EState );
	xc = EState.xt;
	yc = EState.yt;
	Wx_h = EState.Hxt;
	Hy_h = EState.Hyt;
	/* 模型更新 */
	ModelUpdate( EState, ModelHist, nbin, Pi_Thres,	image, W, H );

	/* 计算最大权重值 */
	max_weight = weights[0];
	for ( i = 1; i < NParticle; i++ )
		max_weight = max_weight < weights[i] ? weights[i] : max_weight;
	/* 进行合法性检验,不合法返回-1 */
	if ( xc < 0 || yc < 0 || xc >= W || yc >= H ||
		Wx_h <= 0 || Hy_h <= 0 ) return( -1 );
	else 
		return( 1 );		
}



//把iplimage 转到img 数组中,BGR->RGB
void IplToImge(IplImage* src, int w,int h)
{
	int i,j;
	for ( j = 0; j < h; j++ ) // 转成正向图像
		for ( i = 0; i < w; i++ )
		{
			img[ ( j*w+i )*3 ] = R(src,i,j);
			img[ ( j*w+i )*3+1 ] = G(src,i,j);
			img[ ( j*w+i )*3+2 ] = B(src,i,j);
		}
}
void mouseHandler(int event, int x, int y, int flags, void* param)//在这里要注意到要再次调用cvShowImage,才能显示方框
{
	CvMemStorage* storage = cvCreateMemStorage(0);
	CvSeq * contours;
	IplImage* pFrontImg1 = 0;
	int centerX,centerY;
	int delt = 10;
	pFrontImg1=cvCloneImage(pFrontImg);//这里也要注意到如果在 cvShowImage("foreground",pFrontImg1)中用pFrontImg产效果,得重新定义并复制
	switch(event){
	  case CV_EVENT_LBUTTONDOWN:
		  //printf("laskjfkoasfl\n");
		  //寻找轮廓
		  if(pause)
		  {
			  cvFindContours(pFrontImg,storage,&contours,sizeof(CvContour),CV_RETR_EXTERNAL,
				  CV_CHAIN_APPROX_SIMPLE);

			  //在原场景中绘制目标轮廓的外接矩形
			  for (;contours;contours = contours->h_next)   
			  {
				  CvRect r = ((CvContour*)contours)->rect;
				  if(x>r.x&&x<(r.x+r.width)&&y>r.y&&r.y<(r.y+r.height))
				  {
					  if (r.height*r.width>CONTOUR_MIN_AREA && r.height*r.widthorigin)
//	{
//		image->origin = 0;
//		y = image->height - y;
//	}
//	if(selecting) //正在选择物体
//	{
//		selection.x = MIN(x,origin.x);
//		selection.y = MIN(y,origin.y);
//		selection.width = selection.x + CV_IABS(x - origin.x);
//		selection.height = selection.y + CV_IABS(y - origin.y);
//
//		selection.x = MAX(selection.x ,0);
//		selection.y = MAX(selection.y,0);
//		selection.width = MIN(selection.width,image->width);
//		selection.height = MIN(selection.height,image->height);
//		selection.width -= selection.x;
//		selection.height -= selection.y;
//	}
//	switch(event)
//	{
//	case CV_EVENT_LBUTTONDOWN:
//		origin = cvPoint(x,y);
//		selection = cvRect(x,y,0,0);
//		selecting = 1;
//		break;
//	case CV_EVENT_LBUTTONUP:
//		selecting = 0;
//		if(selection.width >0 && selection.height >0)
//			selected = 1;
//		break;
//	}
//}

void main()
{
	int FrameNum=0;  //帧号
	int k=0;
	CvCapture *capture = cvCreateFileCapture("test.avi");
	char res1[20],res2[20];
	//CvCapture *capture = cvCreateFileCapture("test1.avi");
	//CvCapture *capture = cvCreateFileCapture("camera1_mov.avi");
	IplImage* frame[Num]; //用来存放图像
	int i,j;
	uchar key = false;      //用来设置暂停
	float rho_v;//表示相似度
	float max_weight;

	int sum=0;    //用来存放两图像帧差后的值
	for (i=0;iheight;
				col=curframe->width;
				Mat_D=cvCreateMat(row,col,CV_32FC1);
				cvSetZero(Mat_D);  
				Mat_F=cvCreateMat(row,col,CV_32FC1);
				cvSetZero(Mat_F);
				Wid = curframe->width;
				Hei = curframe->height; 
				img = new unsigned char [Wid * Hei * 3];
			}
			frame[k]=cvCloneImage(curframe);  //把前num帧存入到图像数组
			pTrackImg = cvCloneImage(curframe);
		}
		else
		{
			k=FrameNum%Num;
			pTrackImg = cvCloneImage(curframe);
			IplToImge(curframe,Wid,Hei);
			cvCvtColor(curframe,curFrameGray,CV_RGB2GRAY);
			cvCvtColor(frame[k],frameGray,CV_RGB2GRAY);	
			for(i=0;iheight;i++)
				for(j=0;jwidth;j++)
				{
					sum=S(curFrameGray,j,i)-S(frameGray,j,i);
					sum=sum<0 ? -sum : sum;
					if(sum>T)   //文献中公式(1)
					{
						CV_MAT_ELEM(*Mat_F,float,i,j)=1;
					}
					else 
					{
						CV_MAT_ELEM(*Mat_F,float,i,j)=0;
					}

					if(CV_MAT_ELEM(*Mat_F,float,i,j)!=0)//文献中公式(2)
						CV_MAT_ELEM(*Mat_D,float,i,j)=Re;
					else{
						if(CV_MAT_ELEM(*Mat_D,float,i,j)!=0)
							CV_MAT_ELEM(*Mat_D,float,i,j)=CV_MAT_ELEM(*Mat_D,float,i,j)-1;
					}
					if(CV_MAT_ELEM(*Mat_D,float,i,j)==0.0)
					{
						//文献中公式(3)
						S(pBackImg,j,i)=(uchar)((1-ai)*S(pBackImg,j,i)+ai*S(curFrameGray,j,i));
					}
					sum=S(curFrameGray,j,i)-S(pBackImg,j,i);//背景差分法
					sum=sum<0 ? -sum : sum;
					if(sum>40)
					{
						S(pFrontImg,j,i)=255;
					}
					else 
						S(pFrontImg,j,i)=0;

				}
				frame[k]=cvCloneImage(curframe); 
		}
		FrameNum++;	
		k++;
		cout< 0 && max_weight > 0.0001 )  /* 判断是否目标丢失 */
			{
					cvRectangle(pFrontImg,cvPoint(xout - WidOut,yout - HeiOut),cvPoint(xout+WidOut,yout+HeiOut),cvScalar(255,255,255),2,8,0);
					cvRectangle(pTrackImg,cvPoint(xout - WidOut,yout - HeiOut),cvPoint(xout+WidOut,yout+HeiOut),cvScalar(255,255,255),2,8,0);
					xin = xout; yin = yout;
					WidIn = WidOut; HeiIn = HeiOut;
 				/*draw_rectangle( pBuffer, Width, Height, xo, Height-yo-1, wo, ho, 0x00ff0000, 2 );
 				xb = xo; yb = yo;
 				wb = wo; hb = ho;*/
			}
		}

		cvShowImage("video",curframe);
		cvShowImage("foreground",pFrontImg);
		cvShowImage("background",pBackImg);
		cvShowImage("tracking",pTrackImg);
		/*sprintf(res1,"fore%d.jpg",FrameNum);
		cvSaveImage(res1,pFrontImg);
		sprintf(res2,"ground%d.jpg",FrameNum);
		cvSaveImage(res2,pBackImg);*/
		cvSetMouseCallback("foreground",mouseHandler,0);//响应鼠标
		key = cvWaitKey(1);
		if(key == 'p') pause = true;
		while(pause)
			if(cvWaitKey(0)=='p')
				pause = false;		

	}
	cvReleaseImage(&curFrameGray);
	cvReleaseImage(&frameGray);
	cvReleaseImage(&pBackImg);
	cvReleaseImage(&pFrontImg);
	cvDestroyAllWindows();
//	Clear_MeanShift_tracker();
	ClearAll();
	}

实验结果:

自此,毕业论文涉及的经典算法已经全部给出,我自己提出的破算法就不献丑了。

转载注明来源:CV视觉网 » 基于粒子滤波器的目标跟踪算法及实现

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