  Crowd Counting is a difficult but important problem in computer vision.
Convolutional Neural Networks based on estimating the density map over the
image has been highly successful in this domain. However dense crowd counting
remains an open problem because of severe occlusion and perspective view in
which people can be present at various sizes. In this work, we propose a new
network which uses a ResNet based feature extractor, downsampling block which
uses dilated convolutions and upsampling block using transposed convolutions.
We present a novel aggregation module which makes our network robust to the
perspective view problem. We present the optimization details, loss functions
and the algorithm used in our work. On evaluating on ShanghaiTech, UCF-CC-50
and UCF-QNRF datasets using MSE and MAE as evaluation metrics, our network
outperforms previous state of the art approaches while giving uncertainty
estimates in a principled bayesian manner.
