  Crowd counting is a challenging task due to the heavy occlusions, scales, and
density variations. Existing methods handle these challenges effectively while
ignoring low-resolution (LR) circumstances. The LR circumstances weaken the
counting performance deeply for two crucial reasons: 1) limited detail
information; 2) overlapping head regions accumulate in density maps and result
in extreme ground-truth values. An intuitive solution is to employ
super-resolution (SR) pre-processes for the input LR images. However, it
complicates the inference steps and thus limits application potentials when
requiring real-time. We propose a more elegant method termed Multi-Scale
Super-Resolution Module (MSSRM). It guides the network to estimate the lost de
tails and enhances the detailed information in the feature space. Noteworthy
that the MSSRM is plug-in plug-out and deals with the LR problems with no
inference cost. As the proposed method requires SR labels, we further propose a
Super-Resolution Crowd Counting dataset (SR-Crowd). Extensive experiments on
three datasets demonstrate the superiority of our method. The code will be
available at https://github.com/PRIS-CV/MSSRM.git.
