主要介绍了基于（单、多）高斯统计模型的背景获取法

clear
I=cell(1);
I{1}=imread('021.bmp');     I{2}=imread('031.bmp');      %读入图像    
I{3}=imread('041.bmp');     I{4}=imread('051.bmp');     
I{5}=imread('061.bmp');     I{6}=imread('071.bmp');
I{7}=imread('081.bmp');     I{8}=imread('091.bmp');     
I{9}=imread('101.bmp');     I{10}=imread('111.bmp');    
I{11}=imread('121.bmp');    I{12}=imread('131.bmp');
I{13}=imread('141.bmp');    I{14}=imread('151.bmp');    
I{15}=imread('161.bmp');    I{16}=imread('171.bmp');    
I{1}=im2double(I{1});       I{2}=im2double(I{2});      %转化为双精度型      
I{3}=im2double(I{3});       I{4}=im2double(I{4});       
I{5}=im2double(I{5});       I{6}=im2double(I{6});
I{7}=im2double(I{7});       I{8}=im2double(I{8});       
I{9}=im2double(I{9});       I{10}=im2double(I{10});     
I{11}=im2double(I{11});     I{12}=im2double(I{12});
I{13}=im2double(I{13});     I{14}=im2double(I{14});     
I{15}=im2double(I{15});     I{16}=im2double(I{16});
J=I{1};
for i=2:16
    J=J+I{i};                                 %将十六帧图像求和
end 
J=J/16;                                       %求样本均值                             
Iu=cell(1);
Iu{1}=abs(I{1}-J);         Iu{2}=abs(I{2}-J);     %求样本方差
Iu{3}=abs(I{3}-J);         Iu{4}=abs(I{4}-J);
Iu{5}=abs(I{5}-J);         Iu{6}=abs(I{6}-J);
Iu{7}=abs(I{7}-J);         Iu{8}=abs(I{8}-J);
Iu{9}=abs(I{9}-J);         Iu{10}=abs(I{10}-J);
Iu{11}=abs(I{11}-J);       Iu{12}=abs(I{12}-J); 
Iu{13}=abs(I{13}-J);       Iu{14}=abs(I{14}-J);
Iu{15}=abs(I{15}-J);       Iu{16}=abs(I{16}-J);
Iu{1}=Iu{1}.^2;            Iu{2}=Iu{2}.^2;
Iu{3}=Iu{3}.^2;            Iu{4}=Iu{4}.^2;
Iu{5}=Iu{5}.^2;            Iu{6}=Iu{6}.^2;
Iu{7}=Iu{7}.^2;            Iu{8}=Iu{8}.^2;
Iu{9}=Iu{9}.^2;            Iu{10}=Iu{10}.^2;
Iu{11}=Iu{11}.^2;          Iu{12}=Iu{12}.^2;
Iu{13}=Iu{13}.^2;          Iu{14}=Iu{14}.^2;
Iu{15}=Iu{15}.^2;          Iu{16}=Iu{16}.^2;
Ju=Iu{1};
for i=2:16
    Ju=Ju+Iu{i};
end
Ju=Ju/16; 
Ju=Ju.^0.5;
B=zeros(288,360);                           %建立一个背景零矩阵
F=cell(1);             
num=zeros(288,360);                         %建立一个计数零矩阵
for k=1:16                                  %共十六帧图像
	for i=1:288
        for j=1:360
            if Ju(i,j)==0
                F{k}(i,j)=1;
            else
                F{k}(i,j)=1/(sqrt(2*pi)*Ju(i,j))*exp(-(I{k}(i,j)-J(i,j))^2/(2*Ju(i,j)^2)); 
% 正态分布概率密度函数
            end
            if F{k}(i,j)>=0.78                %设定阈值
                B(i,j)=B(i,j)+I{k}(i,j);         %将大于阈值的灰度加入背景集合        
                num(i,j)=num(i,j)+1;          %统计加入的灰度的数量
            end
        end
    end
end
Bt=B./num;                                 %将灰度求平均值
imshow(Bt);                                 %显示背景图像
K1=Bt-I{3};
imshow(K1);
K2=Bt-I{8};
imshow(K2);                                 %提取目标图像