CrowdNet: A Deep Convolutional Network for Dense Crowd Counting [article]

Lokesh Boominathan, Srinivas S S Kruthiventi, R. Venkatesh Babu
2016 arXiv   pre-print
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
more » ... e 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.
arXiv:1608.06197v1 fatcat:gmy3evj2cjcbhfjvw3hb4v73j4