在MATLAB中移动阴影移除

Question

我正在尝试使用静态小波变换技术去除视频中的移动阴影＆＃39;如参考文件中所述。我在MATLAB中编码但没有得到预期的输出。我是MATLAB的初学者，所以任何人都可以查看我的代码，看看它是否遵循了论文中提到的所有步骤。

回顾参考文件中所遵循的算法：

（i）将rgb视频帧转换为hsv （ii）将hsv分成单独的组件 （iii）找出当前帧和背景帧的h，s和v分量之间的绝对差值 （iv）将SWT转换应用于差异＆＃39;和＆＃39; v＆＃39; （V）计算swt输出的偏度值 （vi）对于阴影检测：如果＆＃39; v＆＃39;的swt输出大于它的偏度，将这些像素值分配给＆＃39; 1＆＃39;否则＆＃39; 0＆＃39; （vii）对于阴影去除：阈值操作基于swt输出来应用＆＃39; reference paper

function shadowremoval()

obj = setupSystemObjects();

while ~isDone(obj.reader)
    frame = readFrame();
    mask1 = shadow(frame);
    displayTrackingResults();
end


%% Create System Objects

    function obj = setupSystemObjects()
        % Create a video file reader.
        obj.reader = vision.VideoFileReader('visiontraffic.avi');

        % Create two video players, one to display the video,
        % and one to display the foreground mask.
        obj.videoPlayer = vision.VideoPlayer('Position', [10, 250, 700, 400]);
        obj.maskPlayer = vision.VideoPlayer('Position', [720, 250, 700, 400]);


        obj.detector = vision.ForegroundDetector('NumGaussians', 3, ...
            'NumTrainingFrames', 40, 'MinimumBackgroundRatio', 0.7);
    end
%% Read a Video Frame
% Read the next video frame from the video file.
    function frame = readFrame()
        frame = obj.reader.step();
    end
%% Perform the operation to remove shadows 

    function mask1 = shadow(frame)
Background=0.0;
        % Detect foreground.
        mask1 = obj.detector.step(frame);
        mask1 = uint8(repmat(mask1, [1, 1, 3])) .* 255;
        % Apply morphological operations to remove noise and fill in holes.
%         mask1 = imerode(mask1, strel('rectangle', [3,3]));
%         mask1 = imclose(mask1, strel('rectangle', [15, 15])); 
        mask1 = imopen(mask1, strel('rectangle', [15,15]));
        mask1 = imfill(mask1, 'holes');
        % Now let's do the differencing
        alpha = 0.5;
        if frame == 1
            Background = frame;
        else
            % Change background slightly at each frame
  %             Background(t+1)=(1-alpha)*I+alpha*Background
            Background = (1-alpha)* frame + alpha * Background;
        end

        % Do color conversion from rgb to hsv
        x=rgb2hsv(mask1);
        y=rgb2hsv(Background);
        % Split the hsv component to h,s,v value
        Hx = x(:,:,1);
        Sx = x(:,:,2);
        Vx = x(:,:,3);
        Hy = y(:,:,1);
        Sy = y(:,:,2);
        Vy = y(:,:,3);
        % Calculate a difference between this frame and the background.
        dh=(abs(double(Hx) - double(Hy)));
        ds1=(abs(double(Sx) - double(Sy)));
        dv1=(abs(double(Vx) - double(Vy)));
        % Perform the 'swt'
        [as,hs,vs,ds] = swt2(ds1,1,'haar');
        [av,hv,vv,dv] = swt2(dv1,1,'haar');
        %Compute the skewness value of 'swt of v'
        sav=skewness(av(:));
        shv=skewness(hv(:));
        svv=skewness(vv(:));
        sdv=skewness(dv(:));
        %Compute the skewness value of 'swt of s'
        sas=skewness(as(:));
        shs=skewness(hs(:));
        svs=skewness(vs(:));
        sds=skewness(ds(:));
        %Perform the thresholding operation
         b=(av>=sav);
        c=(hv>=shv);
        d=(vv>=svv);
        e=(dv>=sdv);

        f=(as>=sas);
        g=(hs>=shs);
        h=(vs>=svs);
        i=(ds>=sds);

        j=(b&f);
        k=(c&g);
        l=(d&h);
        m=(e&i);
        %Perform the inverse 'swt'operation
        recv = iswt2(b,c,d,e,'haar');
        recs= iswt2(j,k,l,m,'haar');

        de_shadow=cat(3,dh,recs,recv);
        mask1=hsv2rgb(de_shadow);
        mask1=rgb2gray(mask1);
    end
function displayTrackingResults()
        % Convert the frame and the mask to uint8 RGB.
        frame = im2uint8(frame);
        mask1 = uint8(repmat(mask1, [1, 1, 3])) .* 255;
% Display the mask and the frame.
        obj.maskPlayer.step(mask1);        
        obj.videoPlayer.step(frame);
    end
end