opencv_traincascade training time vs input dataset parameters [closed]

asked 2014-11-11 13:03:14 -0500

kpykcb gravatar image

Hi all,

i want to make kind of quick and dirty test of performance of LBP cascade detector/tracker.

I'm generating synthetic dataset for some object. I did 2 tries so far. First one ate all free memory (i have macbook pro, ram = 8gb) and hang the system. That was for 18 samples(18 pos/18 neg) with 300x300 resolution.

First i decided to decrease samples resolution to 90x90, that training continues already ~24h, e.g.:

opencv_traincascade -data data -vec object.vec -bg neg/background.txt -numPos 18 -numNeg 18 -numStages 2 -w 90 -h 90 -featureType LBP

I would like to find out is there any rule of thumb about choosing size and number of samples, to "control" the time of training.

I would like to achieve fast training (max 1-2h), detector performance does not matter for now.

NB> Other hints considering LBP tracker training also welcome. Thanks in advance.

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Closed for the following reason question is not relevant or outdated by sturkmen
close date 2020-11-05 12:13:09.184181