Computer Science ›› 2021, Vol. 48 ›› Issue (6): 118-124.doi: 10.11896/jsjkx.200700107

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Crowd Counting Method Based on Cross-column Features Fusion

LI Jia-qian, YAN Hua   

  1. School of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China
  • Received:2020-07-17 Revised:2020-10-20 Online:2021-06-15 Published:2021-06-03
  • About author:LI Jia-qian,born in 1996,postgraduate.Her main research interests include computer vision and deep learning.(jiiaqian@outlook.com)
    YAN Hua,born in 1971,Ph.D,professor.His main research interests include Intelligent information system and so on.
  • Supported by:
    National Natural Science Foundation of China(11872069).

Abstract: Crowd counting is a challenging subject in computer vision and machine learning.Due to the phenomenon of crowd scale change and scene occlusion,the counting accuracy is low.A crowd counting method based on cross-column features fusion,called cross-column features fusion network(CCFNet),is proposed in this paper.CCFNet fuses features from multiple columns and different receptive fields,and combines with the dilate convolution employing coprime expansion rate.Therefore,CCFNet can not only increase the receptive field but also ensure the continuity of information,so as to adapt to the huge changes in the crowd size better.At the same time,the attention model is introduced to guide the network to focus on the head position in the images.According to the attention score graph,different weights are assigned to different positions to highlight the crowd and weaken the background.Finally,a high-quality density map is obtained.In comparative experiments on the current mainstream population counting datasets,the mean absolute error(MAE) reaches 63.2 and 8.9 on the A and B subsets of the ShanghaiTech dataset,222.1 on the UCF_CC_50 dataset,and 7.1 on the WorldExpo’10 dataset.The results show that the proposed method has better counting accuracy and can adapt to different scenes.Especially for scenes with large scale variation,its effect is better than most of the pre-vious algorithms.

Key words: Attention model, Cross-column features fusion, Crowd counting, Dilate convolution

CLC Number: 

  • TP391
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