山西大学学报（自然科学版）2024，Vol.47Issue(2)：359-368,10.DOI:10.13451/j.sxu.ns.2023044
基于特征去噪的密集人群定位方法
Dense Crowd Localization Method Based on Feature Denoising
摘要
Abstract
The difficulty of dense crowd location analysis is to obtain accurate prediction of individual targets in the image.In dense crowd scenes,small target objects and other objects in the foreground image have characteristic noises caused by mutual occlusion and interference.The human head features learned by traditional crowd location methods are easily affected by feature noise,which may lead to weak discrimination of human head features and inaccurate boundary information acquisition.To solve the above prob-lems,a class aware feature denoising method is proposed for crowd location,which uses the idea of semantic feature decoupling to suppress feature noise and thus to enhance the detection of independent heads.Different from the traditional pixel domain denoising methods aiming at improving image visual quality,the proposed method will denoise multi-scale features in the feature space,pro-mote the model to learn more about target features,and suppress interference features.Through the semantic decoupling of fore-ground target features and background features,the response of head features and background features is enhanced and weakened,re-spectively and the detection performance of independent individual targets can be improved.Experimental results show that the aver-age F1 values of the proposed method on Shanghai Tech,UCF-QNRF and NWPU crowd dense population data sets are 81.2％,72.4％and 77.1％respectively,which shows that it improves the performance of dense population localization.关键词
多尺度/特征去噪/背景干扰抑制/独立个体检测/语义解耦Key words
multi-scale/feature denoising/background interference suppression/independent individual detection/semantic decou-pling分类
信息技术与安全科学引用本文复制引用
温超,贺宏强..基于特征去噪的密集人群定位方法[J].山西大学学报（自然科学版）,2024,47(2):359-368,10.基金项目
国家自然科学基金(62106134) （62106134）
山西省留学回国人员科技活动择优资助项目(20220002) （20220002）
