中圖分類號：TP391.1
文獻(xiàn)標(biāo)識碼：A
DOI:10.19358/j.issn.2097-1788.2023.05.009
引用格式：何晴，楊倩倩，彭思凡,等.基于Transformer和語義增強(qiáng)的人群計數(shù)算法［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2023，42（5）：50-58.

Transformer and semantic enhancement for crowd counting

Abstract： Aiming at the problem of scale variation in crowd images, this paper proposes a crowd counting algorithm based on Transformer and semantic enhancement. Firstly, Transformer is introduced as the backbone of the network. Because it can model the global context and obtain the global receptive field, which can effectively deal with the scale variation. Then, the feature maps of adjacent levels of the backbone network are fused from top to bottom in turn, and the semantic information of multiple levels of feature maps is strengthened in the fusion process. Afterwards the dynamic feature selection of multilevel feature maps is carried out, and the features suitable for density map generation are selected. Finally, the density map is adjusted to resist background interference by attention masks, so as to generate highquality crowd density estimation map. In this paper, a large number of experiments are carried out on ShanghaiTech, UCF_QNRF and JHUCROWD++ datasets to verify the effectiveness of the algorithm. The experimental results show that the proposed algorithm can effectively improve the accuracy and robustness of the model.

Key words : crowd counting; Transformer; semantic enhancement; feature selection