智能系统学报2024，Vol.19Issue(2)：307-315,9.DOI:10.11992/tis.202208048
比例融合与多层规模感知的人群计数方法
Crowd counting method based on proportion fusion and multilayer scale-aware
摘要
Abstract
To deal with the problems of insufficient multiscale feature acquisition,poor fusion,and insufficient utiliza-tion of global features as a result of the changing view angles or distances of crowd images in dense scenes,we propose a crowd counting network based on proportion fusion and multilayer scale-aware.First,the backbone network VGG16 is employed to extract the initial characteristics of the population density.Subsequently,a multilayer scale-aware mod-ule is developed to acquire a rich expression of multiscale information from the crowd.Afterward,a proportional fusion strategy is designed to reconstruct the multiscale information based on the feature weights captured by the convolution layer and extract the significant crowd features.Lastly,convolution regression is utilized to regress the density map.Concurrently,a local consistency loss function is proposed,which improves the similarity between the generated dens-ity map and the real density map by regionalizing the density map and enhances the counting performance.The results of the experiments on multiple population datasets exhibit that the model proposed here surpasses the existing state-of-the-art methods of population density counting and has good generalization in vehicle counting.关键词
人群密度估计与计数/卷积神经网络/多层规模感知/比例融合/局部一致性损失/密度图回归/多尺度信息/空洞卷积Key words
crowd density estimation and counting/convolutional neural network/multilayer scale-aware/proportional fusion/local consistency loss/density map regression/multiscale information/dilated convolution分类
计算机与自动化引用本文复制引用
孟月波,张娅琳,王宙..比例融合与多层规模感知的人群计数方法[J].智能系统学报,2024,19(2):307-315,9.基金项目
陕西省重点研发计划项目(2021SF-429). （2021SF-429）
