  Every crowd counting researcher has likely observed their model output wrong
positive predictions on image regions not containing any person. But how often
do these mistakes happen? Are our models negatively affected by this? In this
paper we analyze this problem in depth. In order to understand its magnitude,
we present an extensive analysis on five of the most important crowd counting
datasets. We present this analysis in two parts. First, we quantify the number
of mistakes made by popular crowd counting approaches. Our results show that
(i) mistakes on background are substantial and they are responsible for 18-49%
of the total error, (ii) models do not generalize well to different kinds of
backgrounds and perform poorly on completely background images, and (iii)
models make many more mistakes than those captured by the standard Mean
Absolute Error (MAE) metric, as counting on background compensates considerably
for misses on foreground. And second, we quantify the performance change gained
by helping the model better deal with this problem. We enrich a typical crowd
counting network with a segmentation branch trained to suppress background
predictions. This simple addition (i) reduces background error by 10-83%, (ii)
reduces foreground error by up to 26% and (iii) improves overall crowd counting
performance up to 20%. When compared against the literature, this simple
technique achieves very competitive results on all datasets, on par with the
state-of-the-art, showing the importance of tackling the background problem.
