HAGN: Hierarchical Attention Guided Network for Crowd Counting

Abstract

In recent years, deep learning based crowd counting networks have achieved significant progress. However, most of them generate rough crowd density maps due to low-resolution features used for estimating crowd distribution, which affects the performance of crowd counting. To solve this problem, in this paper, we propose a Hierarchical Attention Guided Network (HAGN) for crowd counting. We apply the first 13 layers of VGG-16 to extract base features. Then, the extracted features are processed by the Hierarchical Attention Mechanism (HAM), which guided the extracted features to enlarge step by step via our proposed attention guided branch. Finally, the outputs of HAM are fed to 1 × 1 convolutional layer for final crowd density estimation. Experiments are performed on ShanghaiTech and UCF-QNRF datasets, and our HAGN achieves promising performance compared with the other state-of-the-art methods on crowd counting and crowd localization, respectively.