on Jun 20, 2018Pedestrian detection
The following livescript uses MATLAB functionalities to simulate the pedestrian detection application. The pedestrian detection algorithm is implemented using Histogram of Oriented Gradients (HOG) and a linear Support Vector Machine (SVM).
Create a People Detector object with general Classification Model.
detector = vision.PeopleDetector('ClassificationModel', 'UprightPeople_96x48', 'ClassificationThreshold', 2.5);
Read the input image which can be RGB or grayscale.
inImgUMat = nxpvt.imread('data/img_sdk.png');
if isempty(inImgUMat)
fprintf('Failed to open input image: %s.', inImgPath);
return;
end
nxpvt.imshow(inImgUMat);
If the input image if RGB transform if to grayscale.
inImgUMatGray = nxpvt.apexcv.rgb2gray(inImgUMat);
nxpvt.imshow(inImgUMatGray);
Run the pedestrian detector on the grayscale image.
bboxes = step(detector, inImgUMatGray.getData());
Add the resulted information on the original image.
outImgUMat = nxpvt.cv.rectangle(inImgUMat, bboxes, [255, 0 ,0], 5);
nxpvt.imshow(outImgUMat);
