I'm going to train a HOG detector for detect objects (pedestrian head) with SVMLight library. But my question is not about implementation.
I'm wondering how to collect samples. I have to use some videos, and they have 1624x1234px dimension. The pedestrian heads have more or less 20x20px bounding-boxes.
Is the 20x20 pixels too small for a correct HOG feature extraction? Or is suitable for training? The, it has to be power of 2 so.. 36 pixels?
And what about the detection? Can I scale down the videos? As far as I know the detection is done on the training sample and then maybe scaled down. So if I train the detector with 20x20px I can detect objects of 20x20 as minimum, then if I scale down the image as half of the size I can detect object 40x40, but not 10x10 (I should scale up but the noise will be a problem).
And then, what about the computation time?
Any hint will be appreciated..