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Training a soft cascade classifier

Hello

I've built an algorithm for pedestrian detection using openCV tools. To perform classification I use a boosted classifier trained with the CvBoost class.

The problem of this implementation is that I need to feed my classifier the whole set of features I used for training. This makes the algorithm extremely slow, so much that each image takes around 20 seconds to be fully analysed.

I need a different detection structure, and openCV has this Soft Cascade class that seems like exactly what I need. Its basic principle is that there is no need to examine all the features of a testing sample, since a detector can reject most negative samples using a small number of features. The problem is that I have no idea how to train one given a fully labeled set of negative and positive examples.

I find no information about this online, so I am looking for any tips you can give me on how to use this soft cascade to make classification.

Best regards