Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network

TL;DR

This paper introduces a multi-resolution, multi-task crowd counting network that enhances PRM-based methods by using feature fusion and attention mechanisms, significantly improving accuracy on challenging crowd images.

Contribution

It proposes a novel multi-resolution, multi-task network with feature fusion and attention modules to improve PRM-based crowd counting accuracy and robustness.

Findings

Achieves 12.6% RMSE improvement over previous methods.

Outperforms state-of-the-art in cross-dataset evaluations.

Demonstrates effectiveness on four benchmark datasets.

Abstract

The paper focuses on improving the recent plug-and-play patch rescaling module (PRM) based approaches for crowd counting. In order to make full use of the PRM potential and obtain more reliable and accurate results for challenging images with crowd-variation, large perspective, extreme occlusions, and cluttered background regions, we propose a new PRM based multi-resolution and multi-task crowd counting network by exploiting the PRM module with more effectiveness and potency. The proposed model consists of three deep-layered branches with each branch generating feature maps of different resolutions. These branches perform a feature-level fusion across each other to build the vital collective knowledge to be used for the final crowd estimate. Additionally, early-stage feature maps undergo visual attention to strengthen the later-stage channels understanding of the foreground regions. The integration of these deep branches with the PRM module and the early-attended blocks proves to be more effective than the original PRM based schemes through extensive numerical and visual evaluations on four benchmark datasets. The proposed approach yields a significant improvement by a margin of 12.6% in terms of the RMSE evaluation criterion. It also outperforms state-of-the-art methods in cross-dataset evaluations.