西南交通大学学报2024，Vol.59Issue(5)：1176-1183,1214,9.DOI:10.3969/j.issn.0258-2724.20220823
基于多尺度感知的密集人群计数网络
Dense Crowd Counting Network Based on Multi-scale Perception
摘要
Abstract
A dense crowd counting network based on multi-scale perception was proposed to solve the problems of diverse target scales and large-scale changes of crowds in dense crowd scenes.Firstly,since the small-scale targets account for a relatively large proportion of the images,a dilated convolution module was introduced based on the visual geometry group 2016(VGG-16)network to mine the detailed information in the images.Then,by utilizing the multi-scale information of the target,a novel context-aware module was designed to extract the contrast features between different scales.Finally,In view of the continuous change of target scales,the multi-scale feature aggregation module was designed to improve the sampling range of dense scales,enhance the interaction of multi-scale information,and thus improve the model performance.The experimental results show that mean absolute errors(MAEs)of the proposed method are 62.5,6.9,and 156.5,and the root mean square errors(RMSEs)are 95.7,11.0,and 223.3 on ShangHai Tech(Part_A/Part_B)and UCF_CC_50 datasets,respectively.Compared with the optimal method of comparison model,the MAE and RMSE are reduced by 1.1%and 4.3%on the UCF_QNRF dataset and by 8.7%and 13.9%on the NWPU dataset.关键词
人群密度估计/多尺度聚合/空洞卷积/密度图Key words
crowd density estimation/multi-scale aggregation/dilated convolution/density map分类
信息技术与安全科学引用本文复制引用
李恒超,刘香莲,刘鹏,冯斌..基于多尺度感知的密集人群计数网络[J].西南交通大学学报,2024,59(5):1176-1183,1214,9.基金项目
国家自然科学基金项目(62271418) （62271418）
四川省自然科学基金项目(23NSFSC0058) （23NSFSC0058）
