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let me try to explain usage of train_HOG.cpp

what we need to prepare

1. we need positive samples gathered in a directory (as much as we have positive samples we can train a better detector)

here is some good positive image samples from INRIA Person Dataset cropped for train a HOG detector which has 64x128 window size

2. we need Images don't include objects that we want to detect gathered in a directory ( for beginning i advise negative image count should not be less than positive images count)

like the image below ( also taken from INRIA Person Dataset (it has pedestrians but they are too small according to 64x128 window size ))

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lets suppose you put positive samples in c:/pos and negative images in c:/neg

here is some possible cmd line parameter usage

cpp-example-train_HOG -pd=c:/pos -nd=c:/neg

if all your positive samples have the same dimension you will get my_detector.yml

winSize is calculated according to your pos images size (96x160 for positive images above ) using the line below

pos_image_size = pos_image_size / 8 * 8;

lets add -dw -dh params like

cpp-example-train_HOG -dw=64 -dh=128 -pd=c:/pos -nd=c:/neg

now we specified winSize as 64x128 but our positive images are 96x160 so the program uses central 64x128 rect for training

         Rect r = Rect(( img_lst[i].cols - wsize.width ) / 2,
                  ( img_lst[i].rows - wsize.height ) / 2,
                  wsize.width,
                  wsize.height);