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Crowd management is of paramount importance when it comes to preventingstampedes and saving lives, especially in a countries like China and Indiawhere the combined population is a third of the global population. Millions ofpeople convene annually all around the nation to celebrate a myriad of eventsand crowd count estimation is the linchpin of the crowd management system thatcould prevent stampedes and save lives. We present a network for crowd countingwhich reports state of the art results on crowd counting benchmarks. Ourcontributions are, first, a U-Net inspired model which affords us to reportstate of the art results. Second, we propose an independent decodingReinforcement branch which helps the network converge much earlier and alsoenables the network to estimate density maps with high Structural SimilarityIndex (SSIM). Third, we discuss the drawbacks of the contemporary architecturesand empirically show that even though our architecture achieves state of theart results, the merit may be due to the encoder-decoder pipeline instead.Finally, we report the error analysis which shows that the contemporary line ofwork is at saturation and leaves certain prominent problems unsolved.

W-Net: Reinforced U-Net for Density Map Estimation