  Crowd counting problem aims to count the number of objects within an image or
a frame in the videos and is usually solved by estimating the density map
generated from the object location annotations. The values in the density map,
by nature, take two possible states: zero indicating no object around, a
non-zero value indicating the existence of objects and the value denoting the
local object density. In contrast to traditional methods which do not
differentiate the density prediction of these two states, we propose to use a
dedicated network branch to predict the object/non-object mask and then combine
its prediction with the input image to produce the density map. Our rationale
is that the mask prediction could be better modeled as a binary segmentation
problem and the difficulty of estimating the density could be reduced if the
mask is known. A key to the proposed scheme is the strategy of incorporating
the mask prediction into the density map estimator. To this end, we study five
possible solutions, and via analysis and experimental validation we identify
the most effective one. Through extensive experiments on five public datasets,
we demonstrate the superior performance of the proposed approach over the
baselines and show that our network could achieve the state-of-the-art
performance.
