  Crowd management is of paramount importance when it comes to preventing
stampedes and saving lives, especially in a countries like China and India
where the combined population is a third of the global population. Millions of
people convene annually all around the nation to celebrate a myriad of events
and crowd count estimation is the linchpin of the crowd management system that
could prevent stampedes and save lives. We present a network for crowd counting
which reports state of the art results on crowd counting benchmarks. Our
contributions are, first, a U-Net inspired model which affords us to report
state of the art results. Second, we propose an independent decoding
Reinforcement branch which helps the network converge much earlier and also
enables the network to estimate density maps with high Structural Similarity
Index (SSIM). Third, we discuss the drawbacks of the contemporary architectures
and empirically show that even though our architecture achieves state of the
art results, the merit may be due to the encoder-decoder pipeline instead.
Finally, we report the error analysis which shows that the contemporary line of
work is at saturation and leaves certain prominent problems unsolved.
