交通信息与安全2023，Vol.41Issue(6)：107-113,7.DOI:10.3963/j.jssn.1674-4861.2023.06.012
交通场景中基于注意力机制神经网络的人群计数
Crowd Count Neural Network Based on Attention Mechanism in Traffic Scenes
摘要
Abstract
Crowd count is an important task in computer vision.Crowd count task in traffic scenes plays a signifi-cant role in maintaining public traffic safety and achieving traffic intelligence.However,crowd count in public traf-fic scenes faces difficulties due to pedestrian occlusion and complex background.In order to achieve high accuracy crowd count,an attention-based crowd density estimation network is proposed.The network consists of three parts:a feature extraction module is designed to generate multi-scale feature maps,which can enhance the feature repre-sentation capability and improve the robustness to pedestrian scale variation of the network;an attention module is designed to suppress the background noise response and enhance the crowd feature response,generate the probabili-ty distribution of the crowd region in the feature map,which can enhance the ability of the network to distinguish the crowd region from the background region;a density estimation module is designed that guides the network to re-gress a high-resolution crowd density map under the constraint of attention mechanism,which can improve the sen-sitivity of the network to crowd regions.In addition,a background-aware structure loss function is designed to re-duce the model false recognition rate and improve the model counting accuracy;meanwhile,a multi-level super-vi-sion mechanism is adopted to guide the network for learning,which can help gradient back-propagation and reduce over-fitting,further improving the network's crowd count accuracy.Experiments are carried out on public dataset ShanghaiTech.Compared with the state-of-the-art algorithms,on ShanghaiTechA and ShanghaiTechB datasets,the mean absolute error(MAE)improves by 2.4%and 1.5%,and the mean square error(MSE)improves by 3.3%and 0.9%,respectively,which demonstrates the superior accuracy and robustness of the proposed algorithm in both crowded and sparse scenes.Experiments are also conducted on real scene dataset with MAE=7.7 and MSE=12.6,which proves the good applicability of the proposed algorithm.关键词
交通安全/人群计数/注意力机制/背景感知结构损失/多级监督机制Key words
traffic safety/crowd count/attention mechanism/background-aware structure loss algorithm/multi-lev-el supervision分类
信息技术与安全科学引用本文复制引用
王丽园,姚韵涛,贾洋,肖进胜,李必军..交通场景中基于注意力机制神经网络的人群计数[J].交通信息与安全,2023,41(6):107-113,7.基金项目
湖北省重点研发计划项目(2023BAB022),中国交通建设集团有限公司科技研发项目(编号2019-ZJKJ-ZDZX02) （2023BAB022）
