Hey guys,
I came across Adaboost in OpenCV and would like to know more about it. I have a good intuition about the algorithm, thanks to the numerous tutorials in the web. But there is this one thing I am not able to catch and would really be grateful if someone could help.
The parameters,
dmin -> minimum acceptable detection per cascade stage
threshold -> Parameter tweaked to make sure that dmin is honored.
fi -> False positive rate per cascade stage evaluated at the current threshold
fmax -> Maximum acceptable false positive rate per cascade stage
are seriously creeping me out.
It would be really helpful if someone can point me towards some resource so that i can understand how threshold is estimated such that dmin holds (so that i can update fi for each iteration and exit loop when it falls below fmax thereby having a strong classifier).
I am following Zhu and Avidan's paper (Refer page 3 - Algorithm pseudocode).
Please comment if I need to update my question with any more details.
Hoping to get help. Thanks for trying.
Regards,
Prasanna S
(Someone please do tell me how to use subscript and superscript while typing)