  Crowd counting is a challenging problem especially in the presence of huge
crowd diversity across images and complex cluttered crowd-like background
regions, where most previous approaches do not generalize well and consequently
produce either huge crowd underestimation or overestimation. To address these
challenges, we propose a new image patch rescaling module (PRM) and three
independent PRM employed crowd counting methods. The proposed frameworks use
the PRM module to rescale the image regions (patches) that require special
treatment, whereas the classification process helps in recognizing and
discarding any cluttered crowd-like background regions which may result in
overestimation. Experiments on three standard benchmarks and cross-dataset
evaluation show that our approach outperforms the state-of-the-art models in
the RMSE evaluation metric with an improvement up to 10.4%, and possesses
superior generalization ability to new datasets.
