Crowd density estimation via multi-scale convolutional neural network in single-image - 文章详情页

作者：

Fu, Yuhao (Fu, Yuhao.) | Wang, Suyu (Wang, Suyu.) | Yang, Bin (Yang, Bin.) | Yu, Chen (Yu, Chen.)

收录：

摘要：

In　the　field　of　video　surveillance,　the　use　of　artificial　intelligence　to　estimate　crowd　density　in　public　places　has　been　a　popular　study.　In　order　to　improve　the　accuracy　of　crowd　density　estimates,　a　multi-scale　convolution　neural　network　structure　is　proposed.　And　the　feature　fusion　of　different　receptive　field　information　is　performed　by　using　multi-column　convolution　network,　and　the　hierarchical　semantic　information　with　different　feature　maps　at　different　resolutions　is　merged　to　generate　a　crowd　density　map　with　higher　quality.　The　experiment　was　tested　on　the　Shanghaitech　dataset,　UCF-CC50　dataset,　and　WorldExpo＇10　dataset　with　mean　absolute　error　(MAE)　and　mean　square　error　(MSE)　as　the　evaluation　criteria.　The　results　show　that　the　new　network　model　reduce　the　value　MAE　and　MSE,　improving　the　accuracy　of　crowd　density　estimation.　©　2019　IEEE.

关键词：

Agricultural robots Convolution Convolutional neural networks Mean square error Robotics Robots Security systems Semantics

作者机构：

[ 1 ] [Fu, Yuhao]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 2 ] [Wang, Suyu]Beijing University of Technology, Beijing Engineering Research Center for IoT Software and Systems, Faculty of Information Technology, Beijing, China
[ 3 ] [Yang, Bin]Beijing University of Technology, Beijing Engineering Research Center for IoT Software and Systems, Faculty of Information Technology, Beijing, China
[ 4 ] [Yu, Chen]Beijing University of Technology, Beijing Engineering Research Center for IoT Software and Systems, Faculty of Information Technology, Beijing, China

通讯作者信息：

电子邮件地址：

查看成果更多字段

成果类型
所属机构

所有年份指定年份从至