  The paper focuses on improving the recent plug-and-play patch rescaling
module (PRM) based approaches for crowd counting. In order to make full use of
the PRM potential and obtain more reliable and accurate results for challenging
images with crowd-variation, large perspective, extreme occlusions, and
cluttered background regions, we propose a new PRM based multi-resolution and
multi-task crowd counting network by exploiting the PRM module with more
effectiveness and potency. The proposed model consists of three deep-layered
branches with each branch generating feature maps of different resolutions.
These branches perform a feature-level fusion across each other to build the
vital collective knowledge to be used for the final crowd estimate.
Additionally, early-stage feature maps undergo visual attention to strengthen
the later-stage channels understanding of the foreground regions. The
integration of these deep branches with the PRM module and the early-attended
blocks proves to be more effective than the original PRM based schemes through
extensive numerical and visual evaluations on four benchmark datasets. The
proposed approach yields a significant improvement by a margin of 12.6% in
terms of the RMSE evaluation criterion. It also outperforms state-of-the-art
methods in cross-dataset evaluations.
