Accuracy parameter. Increasing it, makes the classifier detect less false positives but at the same time the false negative score increases.
Amount of stages in a cascade. Each cascade consists of stumps i.e. trained features.
The overall amount of stumps in all the stages of cascade.
The overall amount of different features used by cascade. Two stumps can use the same features but has different trained values.
The width of a detection window that is used. Objects smaller than this window can't be detected.
The height of a detection window.
A link to the c array that stores stages information using Stage struct.
Link to the c array that stores MBLBP features using MBLBP struct.
The ling to the array with look-up tables that are used by trained MBLBP features (MBLBPStumps) to evaluate a particular region.
The main idea behind cascade of classifiers is to create classifiers of medium accuracy and ensemble them into one strong classifier instead of just creating a strong one. The second advantage of cascade classifier is that easy examples can be classified only by evaluating some of the classifiers in the cascade, making the process much faster than the process of evaluating a one strong classifier.
Class for cascade of classifiers that is used for object detection.
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