Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (9): 235-241.doi: 10.11896/jsjkx.220800067

• Database & Big Data & Data Science • Previous Articles     Next Articles

Crowd Counting Based on Multi-scale Feature Aggregation in Dense Scenes

LIU Peigang1, SUN Jie1, YANG Chaozhi1, LI Zongmin1,2   

  1. 1 School of Computer Science and Technology in China University of Petroleum(East China),Qingdao,Shandong 266580,China
    2 Shengli College of China University of Petroleum,Dongying,Shandong 257061,China
  • Received:2022-08-06 Revised:2022-12-07 Online:2023-09-15 Published:2023-09-01
  • About author:LIU Peigang,born in 1979,Ph.D,postgraduate supervisor,is a member of China Computer Federation.His main research interests include graphical image processing and data science and applications.
  • Supported by:
    National Key R & D Program of China(2019YFF0301800),National Natural Science Foundation of China (61379106) and Shandong Provincial Natural Science Foundation(ZR2013FM036,ZR2015FM011).

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Abstract: Individual scales vary greatly in dense scenes,and the varying scales of target individuals lead to poor crowd counting accuracy.To address this problem,the crowd counting method based on multi-scale feature fusion in dense scenes is proposed.The method investigates the ability of different feature layers to represent feature information for individuals at different scales,with adequate access to multi-scale features through layer connections.At the same time,a multi-scale feature aggregation module is proposed,which uses multiple columns of dilated convolution with different expansion rates,and automatically adjusts the perceptual field through a dynamic feature selection mechanism to effectively extract features of individuals at different scales.The method can further expand the field of perception while preserving the information of small-scale,and improving the detection capability of large-scale individuals,making it better adapted to the multi-scale changes of the population.Experimental results on the three public population counting datasets show that the proposed model has further improved the counting accuracy,with an MAE of 51.21 and an MSE of 83.70 on the ShanghaiTech Part A dataset.

Key words: Intensive scenes, Crowd counting, Dilated convolution, Dynamic feature selection, Point prediction

CLC Number: 

  • TP391.41
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