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 | 1 | initial version |
That is indeed how the boosting principle works. However it more like, we want to build a face detector, and in order to do so we combine weak classifiers that look at ears, nose, mouth, hair, eyebrows, ... but in theory that principle could indeed be expanded towards a larger main class with more subclasses like pedestrians. In principle that is what ICF/ACF does, without telling the classifiers what to look for first. You could force classifiers to look for specific parts in your seperate weak stages of classifiers inside the cascade and that way force the boosting to look for features related to specific parts of an object.
| | 2 | No.2 Revision |
That is indeed how the boosting principle works. However it more like, we want to build a face detector, and in order to do so we combine weak classifiers that look at ears, nose, mouth, hair, eyebrows, ... but in theory that principle could indeed be expanded towards a larger main class with more subclasses like pedestrians. In principle that is what ICF/ACF does, without telling the classifiers what to look for first. You could force classifiers to look for specific parts in your seperate weak stages of classifiers inside the cascade and that way force the boosting to look for features related to specific parts of an object.
Doing this from command line, without any combinational programming will be impossible, but what you can do
However one could wonder if this will be more efficient than simply going for a person at once! A benefit I see is that you could weight the separate cascades and through that way somehow cover partial occlusion.
