中圖分類號： TP309
文獻標識碼： A
DOI： 10.20044/j.csdg.2097-1788.2022.01.017
引用格式： 楊倩倩，何晴，彭思凡，等. 基于多重注意力引導的人群計數(shù)算法[J].網(wǎng)絡安全與數(shù)據(jù)治理，2022，41(1)：108-116.

Multi-attention convolutional network for crowd counting

Abstract： Aiming at the problem of non-uniform crowd distribution in practical scenes, this paper proposes a crowd counting algorithm based on multi-attention mechanism. A top-down feature fusion path is constructed based on the lightweight pyramid split attention mechanism, which aims to promote the fusion of high-level semantic features and low-level spatial details, resulting in high-quality feature maps with both semantics and spatial details. Then multi-scale context information is extracted and fused to generate attention weight maps that focus on different density distribution patterns. At last, the density regression network is guided by the attention weight maps to identify pedestrian targets in different distributions, enhancing the model′s adaptability to density variation, so as to generate high-quality crowd density maps. Abundant experiments on three datasets including ShanghaiTech, UCF_QNRF and JHU-CROWD++ were conducted to prove the effectiveness of the proposed network.

Key words : crowd counting；density map estimation；attention mechanism；feature pyramid network