In the paper Histograms of Oriented Gradients for Human Detection (Navneet Dalal and Bill Triggs) (see link below), to visualize their results, they use a ROC curve, on which the Y axis is TP and the X axis is FPPW (False Positives Per Window).
What is the meaning of this phrase FFPW?
I thought about 3 possible options... I don't know - maybe all of them are wrong. Your help will be appreciated:
Maybe it is the rate of incorrectly classified negative samples, which is: (NUMBER_OF_FALSE_POSITIVES / NUMBER_OF_NEGATIVE_SAMPLES)
Or maybe it is the rate of false alarms per true alarms, which is: (NUMBER_OF_FALSE_POSITIVES / NUMBER_OF_TRUE_POSITIVES)
Or maybe it is the rate of false alarms per true windows in the whle image,
which is: (NUMBER_OF_FALSE_POSITIVES / NUMBER_OF_TRUE_SAMPLES)
I'll be glad to know whether one of them is the correct one, or if you know any other correct definition.
Link to the paper: (https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf)
It appears to be defined as NUMBER_OF_FALSE_POSITIVES / NUMBER_OF_WINDOWS, where the detection window is a 64x128 moving window. Notice in the last paragraph of section 4 it states:
... In a multiscale detector it corresponds to a raw error rate of about 0.8 false positives per 640×480 image tested.