sliding window technique for multiple people detection

◇◆丶佛笑我妖孽 提交于 2019-11-29 17:25:10

The Computer Vision System Toolbox includes vision.PeopleDetector object which uses a sliding window HoG-SVM algorithm.

I am not sure if you are testing the model correctly. Here you have a complete example, and this is the main code for sliding window:

topLeftRow = 1;
topLeftCol = 1;
[bottomRightCol bottomRightRow d] = size(im);

fcount = 1;

% this for loop scan the entire image and extract features for each sliding window
for y = topLeftCol:bottomRightCol-wSize(2)   
    for x = topLeftRow:bottomRightRow-wSize(1)
        p1 = [x,y];
        p2 = [x+(wSize(1)-1), y+(wSize(2)-1)];
        po = [p1; p2];
        img = imcut(po,im);     
        featureVector{fcount} = HOG(double(img));
        boxPoint{fcount} = [x,y];
        fcount = fcount+1;
        x = x+1;
    end
end

lebel = ones(length(featureVector),1);
P = cell2mat(featureVector);
% each row of P' correspond to a window
[~, predictions] = svmclassify(P',lebel,model); % classifying each window

[a, indx]= max(predictions);
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