This forum is disabled, please visit https://forum.opencv.org
 | 1 | initial version |
Classification techniques such as Haar Cascade assume that in a vast training set of samples it is impossible to classify correctly every single sample. So, in most cases, using haar cascade classification means accepting that there will be a number of false positives (negative examples classified as valid objects) and false negatives (positive examples not classified as such).
The False Alarm rate in the input cascade parameters is the percentage of false positives you are allowing the classifier to make in each stage during training. Setting FA to 0 while demanding a very high positive hit-rate will in many cases make it impossible for the training process to end.
