Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting

TL;DR

This paper introduces MSCANet, a novel multi-scale context aggregation network with attention guidance for improved crowd counting, effectively handling scale variation and background noise to produce accurate density maps.

Contribution

The paper proposes a dense context-aware module and hierarchical attention-guided decoder, enhancing multi-scale feature aggregation and noise suppression in crowd counting models.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively handles scale variation and background clutter

Produces high-quality density maps

Abstract

Crowd counting aims to predict the number of people and generate the density map in the image. There are many challenges, including varying head scales, the diversity of crowd distribution across images and cluttered backgrounds. In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single-column encoder-decoder architecture for crowd counting, which consists of an encoder based on a dense context-aware module (DCAM) and a hierarchical attention-guided decoder. To handle the issue of scale variation, we construct the DCAM to aggregate multi-scale contextual information by densely connecting the dilated convolution with varying receptive fields. The proposed DCAM can capture rich contextual information of crowd areas due to its long-range receptive fields and dense scale sampling. Moreover, to suppress the background noise and generate a high-quality density map, we adopt a hierarchical attention-guided mechanism in the decoder. This helps to integrate more useful spatial information from shallow feature maps of the encoder by introducing multiple supervision based on semantic attention module (SAM). Extensive experiments demonstrate that the proposed approach achieves better performance than other similar state-of-the-art methods on three challenging benchmark datasets for crowd counting. The code is available at https://github.com/KingMV/MSCANet