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I want to build my own Object Detection algorithm using HOG feature. Since OpenCV has its own framework to do this for pedestrian detect, I think I can just modify some of the parameters to customize my own one. But I have a few questions about it after reading this.

1.Prepare my own dataset:

Do I have to make all the pos and neg images the same size? Sometimes resize the image may lead to image deformation and affect the hog result. If not, then I have to change the HOG parameters to adapt every image(for example: set the window size = image size and generate a 3780 vector). which one is better.(I prefer the second one)

2.For training the SVM.

In opencv, I think they use SVMLight for training which has been integrated in OpenCV. Can I use Libsvm or other package (compatible with function hog.setSVMDetector())?

3.For hog.detectMultiScale() function

After doing all these above, can I get the result (rectangles) just calling this function?

Thanks!

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user3783676
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    You have to crop the images and resize afterwards. Have a look at the INRIA pedestrian dataset to get an idea how yout dataset should look like. All the persons there are located at approx the same position inside the cropped and resized image. You might want to code a Tool to prepare your dataset first. – Micka Aug 14 '14 at 17:21
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    you can find the INRIA dataset and some information about how training is done, here: http://pascal.inrialpes.fr/data/human/ – Micka Aug 15 '14 at 09:03

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