MATLAB目标跟踪_matlab_目标检测_实现对动态车辆的实时检测、跟踪和标记matlab(推荐)_动态车辆检测_车辆跟踪

trafficObj = mmreader('viptraffic.avi'); frameRate = get(trafficObj,'FrameRate'); % video = read(trafficObj); % implay(video, frameRate); darkCarValue = 50; darkCar = rgb2gray(read(trafficObj,71)); noDarkCar = imextendedmax(darkCar, darkCarValue); % imshow(darkCar) figure(1), imshow(noDarkCar) sedisk = strel('disk',2); noSmallStructures = imopen(noDarkCar, sedisk); figure(2) imshow(noSmallStructures) nframes = get(trafficObj, 'NumberOfFrames'); I = read(trafficObj, 1); taggedCars = zeros([size(I,1) size(I,2) 3 nframes], class(I)); for k = 1 : nframes singleFrame = read(trafficObj, k); % Convert to grayscale to do morphological processing. I = rgb2gray(singleFrame); % Remove dark cars. noDarkCars = imextendedmax(I, darkCarValue); % Remove lane markings and other non-disk shaped structures. noSmallStructures = imopen(noDarkCars, sedisk); % Remove small structures. noSmallStructures = bwareaopen(noSmallStructures, 150); % Get the area and centroid of each remaining object in the frame. The % object with the largest area is the light-colored car. Create a copy % of the original frame and tag the car by changing the centroid pixel % value to red. L = bwlabel(noSmallStructures); taggedCars(:,:,:,k) = singleFrame; if any(L(:)) stats = regionprops(L, {'centroid','area'}); areaArray = [stats.Area]; [junk,idx] = max(areaArray); c = stats(idx).Centroid; c = floor(fliplr(c)); width = 2; row = c(1)-width:c(1)+width; col = c(2)-width:c(2)+width; taggedCars(row,col,1,k) = 255; taggedCars(row,col,2,k) = 0; taggedCars(row,col,3,k) = 0; end end implay(taggedCars, frameRate);