  We seek to improve crowd counting as we perceive limits of currently
prevalent density map estimation approach on both prediction accuracy and time
efficiency. We leverage multilevel pixelation of density map as it helps
improve SNR of training data and therefore, reduce prediction error. To achieve
a better model, we introduce multilayer gradient fusion for training a
density-aware global count regressor. More specifically, on training stage, a
backbone network receives gradients from multiple branches to learn the density
information, whereas those branches are to be detached to accelerate inference.
By taking advantages of such method, our model improves benchmark results on
public datasets and exhibits itself to be a new solution to crowd counting
problems in practice.
