Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network
This work addresses the problem of accurate crowd counting for applications like surveillance and public safety, but it is incremental as it builds upon existing PRM-based approaches.
The paper tackled improving crowd counting accuracy in challenging images by proposing a multi-resolution fusion and attention network integrated with a plug-and-play patch rescaling module, resulting in a 12.6% reduction in RMSE and outperforming state-of-the-art methods in cross-dataset evaluations.
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.