# HA-CCN: Hierarchical Attention-based Crowd Counting Network

## TL;DR

The paper introduces HA-CCN, a hierarchical attention-based network for crowd counting that improves accuracy in congested scenes and adapts to new datasets with weak supervision, achieving state-of-the-art results.

## Contribution

It presents a novel attention mechanism framework integrated with VGG16 for crowd counting and a weakly supervised adaptation method for cross-dataset performance.

## Key findings

- Achieves state-of-the-art crowd counting accuracy.
- Effectively adapts to different datasets with weak supervision.
- Enhances features using spatial and global attention modules.

## Abstract

Single image-based crowd counting has recently witnessed increased focus, but many leading methods are far from optimal, especially in highly congested scenes. In this paper, we present Hierarchical Attention-based Crowd Counting Network (HA-CCN) that employs attention mechanisms at various levels to selectively enhance the features of the network. The proposed method, which is based on the VGG16 network, consists of a spatial attention module (SAM) and a set of global attention modules (GAM). SAM enhances low-level features in the network by infusing spatial segmentation information, whereas the GAM focuses on enhancing channel-wise information in the higher level layers. The proposed method is a single-step training framework, simple to implement and achieves state-of-the-art results on different datasets. Furthermore, we extend the proposed counting network by introducing a novel set-up to adapt the network to different scenes and datasets via weak supervision using image-level labels. This new set up reduces the burden of acquiring labour intensive point-wise annotations for new datasets while improving the cross-dataset performance.