1,178 64×128 sized images of pedestrians (positive samples) and 4,530 of the same sized negative samples were extracted from the INRIA dataset and divided randomly into two to give a training set and a validation set. HoG features on the training images were used to train a Random Forests classifier. The number of trees was set to 100 and the maximum depth of each tree set to 5.

For detection, the search was carried out only over one scale (for testing purposes), on a separate set of images collected from the lab.

Completely un-optimized code on a Core-Two duo laptop already runs at 20 fps.