混合高斯背景建模matlab代码

clear all% source = aviread(C:VideoSourcetrafficsan_fran_traffic_30sec_QVGA);source = mmreader(SampleVideo.avi);frameQYT=get(source,NumberOfFrames);% -frame size variables -fr = read(source,1); % 读取第一帧作为背景fr_bw = rgb2gray(fr); % 将背景转换为灰度图像fr_size = size(fr); %取帧大小width = fr_size(2);height = fr_size(1);fg = zeros(height, width);bg_bw = zeros(height, width);% - mog variables -C = 4; % 组成混合高斯的单高斯数目 (一般3-5)M = 0; % 组成背景的数目D = 2.5; % 阈值（一般2.5个标准差）alpha = 0.01; % learning rate 学习率决定更新速度(between 0 and 1) (from paper 0.01)thresh = 0.75; % foreground threshold 前景阈值(0.25 or 0.75 in paper)sd_init = 6; % initial standard deviation 初始化标准差(for new components) var = 36 in paperw = zeros(height,width,C); % initialize weights array 初始化权值数组mean = zeros(height,width,C); % pixel means 像素均值sd = zeros(height,width,C); % pixel standard deviations 像素标准差u_diff = zeros(height,width,C); % difference of each pixel from mean 与均值的差p = alpha/(1/C); % initial p variable 参数学习率(used to update mean and sd)rank = zeros(1,C); % rank of components (w/sd)% -initialize component means and weights 初始化均值和权值-pixel_depth = 8; % 8-bit resolution 像素深度为8位pixel_range = 2pixel_depth -1; % pixel range 像素范围2的7次方0255（# of possible values)for i=1:height for j=1:width for k=1:C mean(i,j,k) = rand*pixel_range; % means random (0-255之间的随机数) w(i,j,k) = 1/C; % weights uniformly dist sd(i,j,k) = sd_init; % initialize to sd_init end endend%- process frames -处理帧-，这里去第八帧 n = 8; fr = read(source,n); % read in frame 读取帧 fr_bw = rgb2gray(fr); % convert frame to grayscale 转换为灰度图像 % calculate difference of pixel values from mean 计算像素差值 for m=1:C u_diff(:,:,m) = abs(double(fr_bw) - double(mean(:,:,m); end % update gaussian components for each pixel 更新每个像素的背景模型 for i=1:height for j=1:width match = 0; for k=1:C if (abs(u_diff(i,j,k) thresh) k=k-1; M=k; end%? 这里有问题，背景权值和大于阈值时，背景建模的数目M取k-1, end % if no components match, create new component 如果没有匹配的模型则创建新模型 if (match = 0) min_w, min_w_index = min(w(i,j,:); mean(i,j,min_w_index) = double(fr_bw(i,j); sd(i,j,min_w_index) = sd_init; end rank = w(i,j,:)./sd(i,j,:); % calculate component rank 计算模型优先级 rank_ind = 1:1:C; % calculate foreground 计算前景 while (match = 0)&(kM)%? 这里用于前景计算的高斯模型应该是C-M,所以这里kM if (abs(u_diff(i,j,rank_ind(k) = D*sd(i,j,rank_ind(k) fg(i,j) = 0; %black = 0 else fg(i,j) = fr_bw(i,j); end k = k+1; if(k=5) k=k-1; break end end end end fi