My guess is that focal loss focuses on outputting high values only for the foreground boxes and the difficult background ones, aiming at balancing the importance given to the foreground and background boxes and trying to ignore the large amount of easily classified background boxes.
Hence in the case of an overwhelming quantity of backgrounds boxes, normalizing against number of foreground boxes (which is kind of close to foreground boxes + smoothed number of difficult boxes) seems to be a better choice than normalizing against number of (foreground boxes + humongous amount of background boxes).
Maybe something slightly more intuitive would be normalizing again 2*(number of foreground boxes) to take into account that there are not only foreground boxes processed by the focal loss but also a kind of equal “equivalent smoothed quantity” of background boxes.
Hope it makes sense, that is just my “intuitive math” interpretation.