Multiblock Local Binary Pattern in object detect module confusion.

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The default setting of OpenCV is to use stumps as weak classifiers, which means we use decisions trees based on only a single feature value. Then the weak features are combined into a stage that has to reach a certain performance, which is indicated by the parameters -maxFalseAlarmRate and -minHitRate.

You can however change how the feature decision trees are built this by playing round with the following parameters

• -maxDepth < max_depth_of_weak_tree > is the parameter that defines how many splits deep a decision tree can go. OpenCV uses stumps because it is the fastest possible decision tree to evaluate and the fast way to benefit the most of the early rejection principle.
• -maxWeakCount < max_weak_tree_count > is the parameter that defines how many weak trees there can be in a single stage before you need to move on. This is to avoid very long stages that take a lot of time and features to calculate and to result into a classifier that does not apply the early rejection principle.

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