CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
This work addresses crowd counting for surveillance and public safety applications, but it is incremental as it builds on existing deep learning methods with multi-scale data augmentation.
The paper tackles the problem of estimating crowd density from static images of highly dense crowds by proposing a deep learning framework that combines deep and shallow fully convolutional networks, achieving state-of-the-art performance on the UCF_CC_50 dataset.
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.