问题
I am working on video surveillance application for detecting people. Currently I am implementing HOG descriptor as the detector. However, I have a problem regarding the sliding window technique. My code is only able to detect single person. I am using also Group Rectangles from MexOpen CV to creat multiple bounding boxes. Anybody has idea how to write sliding window technique to detect multiple object? Thank you.
% Reading the image
im = strcat ('C:\Users\Documents\MATLAB\HOG\HOG\images\16.bmp');
im = imread (im);
win_size= [64, 128];
[lastRightCol lastRightRow d] = size(im);
counter = 1;
%% Scan the window by using sliding window object detection
% this for loop scan the entire image and extract features for each sliding window
% Loop on scales (based on size of the window)
for s=1:0.5:3
disp(strcat('s is',num2str(s)));
X=win_size(1)*s;
Y=win_size(2)*s;
for y = 1:X/4:lastRightCol-Y
for x = 1:Y/4:lastRightRow-X
p1 = [x,y];
p2 = [x+(X-1), y+(Y-1)];
po = [p1; p2] ;
% Croped image and scan it.
crop_px = [po(1,1) po(2,1)];
crop_py = [po(1,2) po(2,2)];
topLeftRow = ceil(min(crop_px));
topLeftCol = ceil(min(crop_py));
bottomRightRow = ceil(max(crop_px));
bottomRightCol = ceil(max(crop_py));
cropedImage = img(topLeftCol:bottomRightCol,topLeftRow:bottomRightRow,:);
% Get the feature vector from croped image using HOG descriptor
featureVector{counter} = getHOGDescriptor(img);
boxPoint{counter} = [x,y,X,Y];
count = counter+1;
x = x+2;
end
end
end
label = ones(length(featureVector),1);
P = cell2mat(featureVector);
% each row of P' correspond to a window
% classifying each window
[~, predictions] = svmclassify(P', label,model);
% set the threshold for getting multiple detection
% the threshold value is 0.7
get_detect = predictions.*[predictions>0.6];
% the the value after sorted
[r,c,v]= find(get_detect);
%% Creating the bounding box for detection
for ix=1:length(r)
rects{ix}= boxPoint{r(ix)};
end
if (isempty(rects))
rects2=[];
else
rects2 = cv.groupRectangles(rects,3,'EPS',0.35);
end
for i = 1:numel(rects2)
rectangle('Position',[rects2{i}(1),rects2{i}(2),64,128], 'LineWidth',2,'EdgeColor','y');
end
end
回答1:
The Computer Vision System Toolbox includes vision.PeopleDetector
object which uses a sliding window HoG-SVM algorithm.
回答2:
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);
来源:https://stackoverflow.com/questions/23235748/sliding-window-technique-for-multiple-people-detection