  Our work proposes a novel deep learning framework for estimating crowd
density from static images of highly dense crowds. We use a combination of deep
and shallow, fully convolutional networks to predict the density map for a
given crowd image. Such a combination is used for effectively capturing both
the high-level semantic information (face/body detectors) and the low-level
features (blob detectors), that are necessary for crowd counting under large
scale variations. As most crowd datasets have limited training samples (<100
images) and deep learning based approaches require large amounts of training
data, we perform multi-scale data augmentation. Augmenting the training samples
in such a manner helps in guiding the CNN to learn scale invariant
representations. Our method is tested on the challenging UCF_CC_50 dataset, and
shown to outperform the state of the art methods.
