  Estimating crowd count in densely crowded scenes is an extremely challenging
task due to non-uniform scale variations. In this paper, we propose a novel
end-to-end cascaded network of CNNs to jointly learn crowd count classification
and density map estimation. Classifying crowd count into various groups is
tantamount to coarsely estimating the total count in the image thereby
incorporating a high-level prior into the density estimation network. This
enables the layers in the network to learn globally relevant discriminative
features which aid in estimating highly refined density maps with lower count
error. The joint training is performed in an end-to-end fashion. Extensive
experiments on highly challenging publicly available datasets show that the
proposed method achieves lower count error and better quality density maps as
compared to the recent state-of-the-art methods.
