Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 183-187.doi: 10.11896/jsjkx.200300012

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Crowd Counting Model of Convolutional Neural Network Based on Multi-task Learning and Coarse to Fine

CHEN Xun-min, YE Shu-han, ZHAN Rui   

  1. College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:CHEN Xun-min,born in 1995,postgra-duate.His main research interests include deep learning and image communication.

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Abstract: Crowd counting refers to counting the number of people in a single image or a single video frame.In order to solve the problem of insufficient counting of crowd tasks,a crowd counting model based on multi-task learning and coarse to fine convolutional neural network is proposed.Firstly,multi-task learning means introducing auxiliary tasks related to the original task to guide the learning of the main tasks.The crowd density estimation is the main task of the crowd counting model,and the crowd segmentation task is used as an auxiliary task to improve network performance.Secondly,the proposed crowd counting model is able to predict the density map from coarse to fine.A rough and inaccurate crowd density map is generated,which is combined with the crowd segmentation map to obtain an accurate crowd density map.Experiments on the Shanghai Tech dataset Part A and Part B,and UCF_CC_50 dataset show that the proposed crowd counting model outperforms the state of the art CSRNet models by 4.55%,14.15% and 19.09% respectively,and the mean square error is reduced by 10.00 %,19.09% and 19.47% respectively compared with the SOTAs.The proposed model significantly improves the accuracy and robustness of the crowd counting model.

Key words: Convolutional neural network, Crowd counting, Crowd density estimation, Crowd segmentation, Multi-task learning

CLC Number: 

  • TP391
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