I have trained a HaarCascade which gives good detection for any test video for a specific MinNeighbors parameter. The problem is that when the detection is tried on a different video, I need to manually adjust the MinNeighbors parameter again to get good detection with less False Positives.
Is it because the number of images in my training data are not distributed equally for various kind of scenes/backgrounds etc (for e.g. I have more day time images than night time, so the parameter is different for day/night test videos)?
Similarly, the positive images are different in terms of image quality and the number is not same for all types of image quality (good/bad/blur: more good less bad/blur).
Is there any way to manage the change of MinNeighbors programatically (avoiding manual change for each video)?