So I'm using OpenCV's LBP detector. The shapes I'm detecting are all roughly circular (differing mostly in aspect ratio), with some wide changes in brightness/contrast, and a little bit of occlusion.
OpenCV's guide on how to train the detector is here
My main question to anyone with experience using it is how numPos and numNeg should be in relation to eachother? I have roughly 1000 positive samples (so ~900 being used per stage)
What I need to decide is how many negative samples to use per stage for training. I have about 20000 images from which to draw negative data, so redundancy isn't really an issue.
In general the rule I hear is 1:2, but that seems like under-utilization, given how much negative data I have at my disposal. On the flip side, what effects should I expect if I train my detector with 1:20? How should I determine the proper ratio?