I've been using Haar Cascades and LBP cascades trained with the opencv_traincascade tool which is brilliant.
I'd like to hear some purposes about how to generate a bigger database which in fact improves the accuracy. What I mean is: let's imagine we've got 2,000 positive images and 10,000 negative images. For CNN (Convolutional Neural Networks) I've rotated, translated and scaled pictures in order to multiplicate those 2,000 into a 8,000 positive samples which really improves the results, but I don't really have clear what I could do for Cascade Training.
My purposes are:
- Generate a part of the positive set with noise. For instance:
- Generate a part of the positive set with highlights or blenders.
Have you used anything else or tried something which could improve the accuracy?
Thank you in advance.
Rafael.
