基于双鉴别器策略的空间感知型人群计数算法  

Spatial-aware crowd counting algorithm based on dual discriminator strategy

作　　者：王雅楠 杨萌萌 张军锋 张慧娟 WANG Yanan;YANG Mengmeng;ZHANG Junfeng;ZHANG Huijuan(Henan Vocational College of Water Conservancy and Environment,Zhengzhou 450008,China)

机构地区：[1]河南水利与环境职业学院,郑州450008

出　　处：《中国科技论文在线精品论文》2024年第3期346-349,共4页Highlights of Sciencepaper Online

基　　金：河南省科技攻关项目(242102211054);河南水利与环境职业学院2024年度校内立项科研项目(SHKYXM2423)。

摘　　要：人群计数是计算机视觉中的重要研究领域,为了精确计算静态图像或视频帧中的行人数,通常采用回归密度图法用于提高计数精度,然而,由于源域和目标域之间的域移位问题,模型在稀缺标签数据集上的泛化能力不足。文章提出了一种基于双鉴别器策略的空间感知型(spatial awareness of dual discriminator strategy,SADDS)域适应人群计数算法,旨在有效解决图像风格和人头尺度的差异。该算法在DSANET骨干网络VGG16的特征提取过程中融合了分层注意力机制(hierarchicalattentionmechanism,HAM),包括空间注意力机制(spatial attention mechanism,SAM)和全局注意力机制(global attention mechanism,GAM),同时采用全局和局部双鉴别器来减少域移位。通过在公开数据集上的实验,验证了该方法的有效性与优越性。Crowd counting is an important research field in computer vision.In order to accurately calculate the number of rows in static images or video frames,the regression density map method is usually used to improve the counting accuracy.However,due to the domain shift problem between the source domain and the target domain,the generalization ability of the model on the scarce label data set is insufficient.This paper proposes a space-aware spatial awareness of dual discriminator strategy(SADDS)domain adaptive crowd counting algorithm based on dual discriminator strategy,which aims to effectively solve the difference between image style and head scale.The algorithm combines hierarchical attention mechanism(HAM),including spatial attention mechanism(SAM)and global attention mechanism(GAM),in the feature extraction process of DSANET backbone network VGG16.At the same time,global and local double discriminators are used to reduce domain shift.The effectiveness and superiority of the method are verified by experiments on public data sets.

关 键 词：计算机应用 人群计数 域适应 注意力机制 双鉴别器策略 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]