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.
@article{arxiv.1608.06197,
title = {CrowdNet: A Deep Convolutional Network for Dense Crowd Counting},
author = {Lokesh Boominathan and Srinivas S S Kruthiventi and R. Venkatesh Babu},
journal= {arXiv preprint arXiv:1608.06197},
year = {2016}
}