基于视频处理的变电站内运动目标检测及 巡检人员着装判别系统matlab完整源码+代码注释.zip

clc; clear all; cntFrame = 100; obj = VideoReader('move2.avi'); numFrames = obj.NumberOfFrames; % for k = 1 : cntFrame % frame = read(obj,k); % imwrite(frame,... % strcat('D:\WTYdoc\MATLAB\graduate\video\',... % num2str(k),'.bmp'),'bmp'); % end %% 参数定义及初始化 I = imread('1.bmp'); %读入第一帧作为背景帧 fr_bw = I; [height,width] = size(fr_bw); %求每帧图像大小 width = width/3; %排除颜色通道数 fg = zeros(height, width); %定义前景和背景矩阵 bg_bw = zeros(height, width); C = 3; % 单高斯模型的个数(通常为3-5) M = 3; % 代表背景的模型个数 D = 1; % 偏差阈值 alpha = 0.01; % 学习率 thresh = 0.25; % 前景阈值 sd_init = 15; % 初始化标准差 w = zeros(height,width,C); % 初始化权重矩阵 mean = zeros(height,width,C); % 像素均值 sd = zeros(height,width,C); % 像素标准差 u_diff = zeros(height,width,C); % 像素与某个高斯模型均值的绝对距离 p = alpha/(1/C); % 初始化p变量，用来更新均值和标准差 rank = zeros(1,C); % 各个高斯分布的优先级（w/sd) pixel_depth = 8; % 每个像素8bit分辨率 pixel_range = 2^pixel_depth -1; % 像素值范围[0,255] for i=1:height for j=1:width for k=1:C mean(i,j,k) = rand*pixel_range; %初始化第k个高斯分布的均值 w(i,j,k) = 1/C; % 初始化第k个高斯分布的权重 sd(i,j,k) = sd_init; % 初始化第k个高斯分布的标准差 end end end for n = 1:3:cntFrame frame=strcat(num2str(n),'.bmp'); I1 = imread(frame); % 依次读入各帧图像 fr_bw = I1; % 计算新像素与第m个高斯模型均值的绝对距离 for m=1:C u_diff(:,:,m) = abs(double(fr_bw(:,:,m)) - double(mean(:,:,m))); end % 更新高斯模型的参数 for i=1:height for j=1:width match = 0; %匹配标记; for k=1:C if (abs(u_diff(i,j,k)) <= D*sd(i,j,k)) % 像素与第k个高斯模型匹配 match = 1; %将匹配标记置为1 % 更新权重、均值、标准差、p w(i,j,k) = (1-alpha)*w(i,j,k) + alpha; p = alpha/w(i,j,k); mean(i,j,k) = (1-p)*mean(i,j,k) + p*double(fr_bw(i,j)); sd(i,j,k) = sqrt((1-p)*(sd(i,j,k)^2) + p*((double(fr_bw(i,j)) - mean(i,j,k)))^2); else % 像素与第k个高斯模型不匹配 w(i,j,k) = (1-alpha)*w(i,j,k); %略微减少权重 end end bg_bw(i,j)=0; for k=1:C bg_bw(i,j) = bg_bw(i,j)+ mean(i,j,k)*w(i,j,k); end % 像素值与任一高斯模型都不匹配，则创建新的模型 if (match == 0) [min_w, min_w_index] = min(w(i,j,:)); %寻找最小权重 mean(i,j,min_w_index) = double(fr_bw(i,j));%初始化均值为当前观测像素的均值 sd(i,j,min_w_index) = sd_init; %初始化标准差为6 end rank = w(i,j,:)./sd(i,j,:); % 计算模型优先级 rank_ind = (1:1:C);%优先级索引 % 计算前景 fg(i,j) = 0; while ((match == 0)&&(k<=M)) if (abs(u_diff(i,j,rank_ind(k))) <= D*sd(i,j,rank_ind(k)))% 像素与第k个高斯模型匹配 fg(i,j) = 0; %该像素为背景，置为黑色 else fg(i,j) = 255; %否则为前景，置为白色 end k = k+1; end end end T=graythresh(fg); fg_bw=im2bw(fg,T); erodeElement = strel('square',1) ; dilateElement=strel('square',4) ; fg_bw=imerode(fg_bw,erodeElement); fg_bw=imdilate(fg_bw,dilateElement); fg_bw=imfill(fg_bw,'holes'); fg_reg=regionprops(fg_bw,'area','boundingbox'); areas=[fg_reg.Area]; rects=cat(1,fg_reg.BoundingBox); figure(n) subplot(1,3,1),imshow(fr_bw); %显示最后一帧图像 for i=1:size(rects,1) if(rects(i,3)>50&&rects(i,4)>60) rectangle('position',rects(i,:),'EdgeColor','r'); end end subplot(1,3,2),imshow(uint8(bg_bw)) %显示背景 subplot(1,3,3),imshow(fg_bw); %显示前景 end