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Hi, in case of HoG you can vary the hit_threshold and group_threshold parameters of the detectMultiScale method to control the number of false positives and true positives.

From the opencv documentation gpu::HOGDescriptor::detectMultiScale (the CPU HoG documentation is currently missing, its a bug in the OpenCV documentation. But the GPU HoG has a quite similar interface so we can look a this one).

hit_threshold - Threshold for the distance between features and SVM classifying plane

group_threshold - Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See groupRectangles()

In my project I am using MeanShiftGrouping=true. MeanShiftGrouping combines the detected rectangles and weights of the detected objects into one result. So we get an resulting rectangle and weight. This weight can be "threshold-ed" by using group_threshold. For the DET diagram in my project I am using therefore a small value for hit_threshold and vary then the group_threshold.

If you use the DET or ROC plot to find the "best" parameter setting for your application you should vary both, the hit_threshold and the group_threshold.