Machine Learning for OpenCV

Detecting pedestrians in a larger image

What's left to do is to connect the SVM classification procedure with the process of detection. The way to do this is to repeat our classification for every possible patch in the image. This is similar to what we did earlier when we visualized decision boundaries; we created a fine grid and classified every point on that grid. The same idea applies here. We divide the image into patches and classify every patch as either containing a pedestrian or not.

Therefore, if we want to do this, we have to loop over all possible patches in an image, each time shifting our region of interest by a small number of stride pixels:

In [23]: stride = 16...      found = []... for ystart in np.arange(0, img_test.shape[0], ...