Hi,
I have been training some LBP Cascade Classifiers and I have noticied that when I use them to detect an object the calculation time depends on the number of images used and also their size.
Is that correct? For exmaple, a trained classifier with 500 positive and 500 negative images gives me about 6 FPS while another classifier trained with they same parameters but only 150 positive and 250 negative images is giving me about 15 FPS.
The detection is done with the same parameters for both of them and fixed min and max size. I thought that that would make the time independent of the training set but, apart from the number of images, when I use images of size 100x100 the time is twice the time it takes when using 40x40 images.
I don't really understand how that can be possible. Any ideas?
Thank you! Carlos.
