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I have some general techniques that seem to work with haar classifiers. The same will likely work for you.

If I'm getting too many false positives, a great tool to use is the opencv_createsamples executable. It allows you to generate many additional positive samples by stretching, rotating and zooming in/out positives. It also throws these samples in other negative images, so your classifier learns to ignore backgrounds. Here's a good tutorial for it.

If I'm not getting enough true positives, I simply need more positives examples. These could be just brute-forcing more examples, or trying more feature-rich positive examples.

I have some general techniques that seem to work with haar classifiers. The same will likely may work for you.

If I'm getting too many false positives, a great tool to use is the opencv_createsamples executable. It allows you to generate many additional positive samples by stretching, rotating and zooming in/out positives. It also throws these samples in other negative images, so your classifier learns to ignore backgrounds. Here's a good tutorial for it.

If I'm not getting enough true positives, I simply need more positives examples. These could be just brute-forcing more examples, or trying more feature-rich positive examples. examples.