I have train my own classifier using 3 different stages , (10, 20, 30). All with 399 positive images, and about 180 negative images. I've tested each the classifier on to a video file, and analyze the ratio of positives and the actual object counts. I got a result that those three give different result, 10 stages gives the bad performance, 20 stages is better. But the 30 stages almost gives nothing, or, almost never detect the object as seen in the 20 stages. How does this could happened? As far as my understanding, the performance should be better if number of boosting stages were increased.
Thanks