运动目标识别系统Matlab仿真——附上程序

本人毕业设计做的是红外运动识别系统，通过Matlab做了仿真。其中视频资源来自优酷。毕设论文随后上传。以下是matlab代码

%.........题目：基于光流场算法的人的检测%.........备注：需要调用计算视觉系统工具箱。%......... 基本实现目标的跟踪,可以做出报警%......... 结果图片储存%.........程序员：青鸟绝迹%.........时间：4月19日clear all%创建必要的视频处理对象videofile = 'people_net.mp4'; %获取视频帧像素info = VideoReader(videofile);cols=info.Width;rows=info.Height;%创建视频系统对象，读取视频文件hReader = vision.VideoFileReader(videofile, 'ImageColorSpace', 'RGB',... 'VideoOutputDataType', 'single'); hFlow = vision.OpticalFlow( 'OutputValue', 'Horizontal and vertical components in complex form', ... 'ReferenceFrameDelay', 3, 'Method','Horn-Schunck');%创建两个均值对象，用于分析光流矢量hMean1 = vision.Mean; hMean2 = vision.Mean('RunningMean', true); %累计平均值，每次输入一个数据，计算所有历史输入的均值%创建中值滤波对象，用来移除对象分割产生的斑点噪声hFilter2 = vision.MedianFilter;hFilter1 = vision.MedianFilter([3 3]);%创建形态学闭运算，填充分割后的目标空洞%hClose = vision.MorphologicalClose('Neighborhood', strel('line',20,90));hClose = vision.MorphologicalClose('Neighborhood', strel('disk',15));%30正好，有点大%创建跟踪对象（BLOB），用于从视频中分割目标%hBlob = vision.BlobAnalysis( 'CentroidOutputPort', false, 'AreaOutputPort', true, ...% 'BoundingBoxOutputPort', true, 'OutputDataType', 'double', 'MinimumBlobArea', 250,...% 'MaximumBlobArea', 3600, 'MaximumCount', 50);hBlob = vision.BlobAnalysis( 'CentroidOutputPort', false, 'AreaOutputPort', true, ... %此参数为最佳无需改动 'BoundingBoxOutputPort', true,'PerimeterOutputPort',true, 'OutputDataType', 'double', 'MinimumBlobArea', 1000,... 'MaximumBlobArea', 20000, 'MaximumCount', 1);%创建形态学腐蚀对象，移除不需要的对象hErode = vision.MorphologicalErode('Neighborhood', strel('square',5));%创建嵌入形状对象，狂出目标边界hShape1 = vision.ShapeInserter( 'BorderColor', 'Custom', 'CustomBorderColor', [0 1 0]); hShape2 = vision.ShapeInserter( 'Shape','Lines', 'BorderColor', 'Custom', ... 'CustomBorderColor', [255 255 0]); %hText = vision.TextInserter( 'Text', '%4d','Location', [1 1], 'Color', [1 1 1], 'FontSize', 12); %创建视频播放对象，用来显示原始视频，运动矢量，分割结果，和最终结果 sz = get(0,'ScreenSize'); pos = [(sz(3)-4*(cols+75))/2, (sz(4)-rows)/2 cols+60 rows+80]; hVideo1 = vision.VideoPlayer('Name','Original Video','Position',pos);pos(1) = pos(1)+cols+75;hVideo2 = vision.VideoPlayer('Name','Motion Vector','Position',pos);pos(1) = pos(1)+cols+75;hVideo3 = vision.VideoPlayer('Name','Thresholded Video','Position',pos);pos(1) = pos(1)+cols+75; hVideo4 = vision.VideoPlayer('Name','Results Video','Position',pos);%从视频中检测跟踪目标%显示光流矢量的像素点[xpos,ypos]=meshgrid(1:3:cols,1:3:rows);%每隔5行5列选择像素点xpos=xpos(:);ypos=ypos(:);locs=sub2ind([rows,cols],ypos,xpos);i = 0; %截图标志位ii = 0; %提示窗标志位j = 0; %图片指针C_k = 0; %长宽比指针S_m = 0; %形状分散度指针%循环处理视频每一帧，直到文件结束while ~isDone(hReader) %暂停0.3s，为肉眼观察 pause(0.3); %从视频文件读取视频帧数 frame = step(hReader); %把图像转换为灰度图像 gray_1 = rgb2gray(frame); gray = step(hFilter1,gray_1); %一、计算光流矢量，返回一个复数矩阵，分别代表每个像素点u和v flow = step(hFlow, gray); %每隔5行5列选择一个像素点，绘制它的光流图。放大20倍 %lines每行对应一条线，分别是第1和第2个点的x，y坐标 %lines = [xpos, ypos, xpos+20*real(flow(locs)), ypos+20*imag(flow(locs))]; lines = [xpos, ypos, xpos+20*real(flow(locs)), ypos+imag(flow(locs))]; %将光流场矢量添加到视频帧上 vector = step(hShape2, frame, lines); %二、计算光流矢量幅值，因为光流是一个复数矩阵，所以要用到共轭相乘 magnitude = flow .* conj(flow); %计算光流幅值平均值，表征速度阈值 threshold = 0.5 * step(hMean2, step(hMean1, magnitude)); %使用阈值分割提取运动对象，然后去掉噪声 %carobj = step(hFilter, magnitude >= threshold); carobj_1 = im2bw(magnitude,threshold); carobj = step(hFilter2,carobj_1); %通过形态学腐蚀去掉背景，然后形态学关闭填补目标（BLOB）孔洞 %carobj = step(hClose, step(hErode, carobj)); %形态学腐蚀 carobj1 = step(hErode, carobj); %闭运算填充 carobj2 = step(hClose, carobj1); %三、统计估计目标（BLOB）的面积area和边框bbox[left,bottom,width,height] [area, bbox,perimeter] = step(hBlob, carobj2); %for j=1:4 %fprintf('%d',bbox(j)) %end %从grow的位置开始统计 grow=0; idx = bbox(:,1) > grow; %% 计算目标中心坐标 即：矩形框的中心坐标 x = bbox(:,1)+int32(bbox(:,3)/2); y = bbox(:,2)+int32(bbox(:,4)/2); %% 对目标进行提取 infeed = 100; %infeed=320; %vertical = 160; %bbox(:,1) if (x >= infeed & i == 0 & x<= (infeed+10)) i = i+1; %perimeter %area %S = perimeter*perimeter/double(area); %C = double(bbox(idx,4))/double(bbox(idx,3)); carobj_new1 = double(imcrop(gray,[bbox(idx,1),bbox(idx,2),bbox(idx,3)+1,bbox(idx,4)+1])); imshow(carobj_new1); end %% 对目标进行识别 C = double(bbox(idx,4))/double(bbox(idx,3)); if area >0 S = perimeter*perimeter/double(area); if S >= 50 S_m = S_m+1; end end if (C >=1.4 & C<=4) C_k = C_k+1; if (C_k>=10 & S_m>=10 & ii==0) errordlg('有人闯入','MATLAB warning'); ii = ii+1; end end %% %计算目标面积和边框面积的百分比 %ratio = zeros(length(idx), 1); ratio = zeros(length(idx), 1); ratio(idx) = single(area(idx,1))./single(bbox(idx,3).*bbox(idx,4)); %当面积大于5%认为是目标 %flag = ratio > 0.4; flag = ratio > 0.3; %统计数量 count = int32(sum(flag)); %bbox(~flag, :) = int32(-1); %bbox(~flag, :) = int32(-1); %给目标添加边框 %result = step(hShape1, frame, bbox); result = step(hShape1, frame,bbox); %result(grow:grow+1,:,:) = 1; %result(1:15,1:30,:) = 0; %result = step(hText, result, count); %显示最终处理结果 %step(hVideo1, frame); %原始视频 %step(hVideo1, gray_1); %添加光流矢量 step(hVideo2, vector); %阈值分割结果 step(hVideo3, result); %带框目标 j = j+1; endrelease(hReader);