Detecting cars

To train any kind of classifier, we must begin by creating or acquiring a training dataset. We are going to train a car detector, so our dataset must contain positive samples that represent cars, as well as negative samples that represent other (non-car) things that the detector is likely to encounter while looking for cars. For example, if the detector is intended to search for cars on a street, then a picture of a curb, a crosswalk, a pedestrian, or a bicycle might be a more representative negative sample than a picture of the rings of Saturn. Besides representing the expected subject matter, ideally, the training samples should represent the way our particular camera and algorithm will see the subject matter.

Ultimately, in this chapter, we intend to use a sliding window of fixed size, so it is important that our training samples conform to a fixed size, and that the positive samples are tightly cropped in order to frame a car without much background.

Up to a point,...