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I am trying to train a cascade to recognize objects
This could be your first issue, a cascade classifier is an object detector, not an object recognition interface, so please explain your case on what you exactly are trying to do.
Does somebody has an idea why this could happen and what can I try to avoid this behavior?
Basically what happens is your algorithm is not able to meet your demands. It can only make 2 mistakes on a 1000 possible samples at it weak classifier. This is already a very harsh requirement. There is simply no feature in your feature set you provided (and from which boosting already selected the best 2000 ones), that is able to do this. This results in a very complex weak classifier that basically does nothing useful.
Can you try with for example 0.95 hit rate to start? I am sure this will not happen in that case. If it does, I am pretty sure you are using the wrong solution for your task because that would mean your 2 classes are inseparable using either HAAR or LBP features of the grayscale input image.
