OpenCV Haar Classifier result table explanation - opencv

Question

I am trying to create a Haar classifier to recognise objects however I can't seem to figure out what the results table that is produced at each stage stands for.
E.g. 1
===== TRAINING 1-stage =====
<BEGIN
POS count : consumed 700 : 700
NEG count : acceptanceRatio 2500 : 0.452161
Precalculation time: 9
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 1|
+----+---------+---------+
| 2| 1| 1|
+----+---------+---------+
| 3| 1| 1|
+----+---------+---------+
| 4| 1| 1|
+----+---------+---------+
| 5| 1| 0.7432|
+----+---------+---------+
| 6| 1| 0.6312|
+----+---------+---------+
| 7| 1| 0.5112|
+----+---------+---------+
| 8| 1| 0.6104|
+----+---------+---------+
| 9| 1| 0.4488|
+----+---------+---------+
END>
E.g. 2
===== TRAINING 2-stage =====
<BEGIN
POS count : consumed 500 : 500
NEG count : acceptanceRatio 964 : 0.182992
Precalculation time: 49
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 1|
+----+---------+---------+
| 2| 1| 1|
+----+---------+---------+
I'm not sure what the N, HR, and FA are referring to in each of these cases. Could someone explain what they stand for and what they mean?

Answer 1

Searching for "HR" in the OpenCV source leads us to this file. Lines 1703-1707 inside CvCascadeBoost::isErrDesired print the table:
cout << "|"; cout.width(4); cout << right << weak->total;
cout << "|"; cout.width(9); cout << right << hitRate;
cout << "|"; cout.width(9); cout << right << falseAlarm;
cout << "|" << endl;
cout << "+----+---------+---------+" << endl;
So HR and FA stand for hit rate and false alarm. Conceptually: hitRate = % of positive samples that are classified correctly as such. falseAlarm = % of negative samples incorrectly classified as positive.
Reading the code for CvCascadeBoost::train, we can see the following while loop
cout << "+----+---------+---------+" << endl;
cout << "| N | HR | FA |" << endl;
cout << "+----+---------+---------+" << endl;
do
{
[...]
}
while( !isErrDesired() && (weak->total < params.weak_count) );
Just looking at this, and not knowing much about the specifics of boosting, we can make the educated guess that training works until error is low enough, as measured by falseAlarm.