中圖分類號： TP391
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.07.010
引用格式： 郝曉亮，楊倩倩，夏殷鋒，等. 基于上下文特征重聚合網(wǎng)絡(luò)的人群計數(shù)[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(7)：59-65.

Context-aware feature reaggregation network for crowd counting

Abstract： Aiming at the problem of the target scale-aware in crowd counting, this paper proposes a counting algorithm based on the reaggregation of contextual features. The semantic information extracted by the high-level network is combined with the crowd-scale detail information extracted by the low-level network, and the purpose is to use the information extracted from the low network to integrate the features of different scales into the features extracted from the deep network, thereby fusing multiple scales crowd feature return to a high-quality crowd density map. In addition, this paper performs algorithm performance verification on ShanghaiTech, UCF_CC_50 and UCF_QNRF datasets and the effectiveness of structures is verified through structural experiments.

Key words : crowd counting；context-aware feature enhance；multi-scale feature fusion；density map