计算机应用与软件2024，Vol.41Issue(6)：194-199,6.DOI:10.3969/j.issn.1000-386x.2024.06.029
结合单列多列神经网络的移动状态人群计数方法研究
MOVING CROWD COUNTING BY INTERGRATING SINGLE AND MULTIPLE COLUMN NEURAL NETWORK
摘要
Abstract
Existing crowd counting methods are limited to counting the integrity of the crowd,the accuracy rate is downgraded when exclusively counting the moving people in the crowd.An attention based multi-stage deep learning framework is proposed to solve this problem.Attention module was adopted to adaptively selects both single-column and multi-column counting networks,combine the deep features of single column network and the multiple scale receptive fields of multiple column network,which effectively extracted features of the moving people.The results show that the proposed method has lower mean square error(MSE)and mean absolute error(MAE)than existing crowd counting methods.The counting accuracy of people on moving is well improved.关键词
人群计数/深度学习/单列多列网络/注意力机制Key words
Crowd counting/Deep learning/Single and multiple column network/Attention mechanism分类
信息技术与安全科学引用本文复制引用
温宇健,郭士杰..结合单列多列神经网络的移动状态人群计数方法研究[J].计算机应用与软件,2024,41(6):194-199,6.基金项目
国家重点研发计划项目(2016YFE0128700) （2016YFE0128700）
河北省重点研发计划项目(18211816D). （18211816D）
