Computer Science ›› 2020, Vol. 47 ›› Issue (4): 184-188.doi: 10.11896/jsjkx.190700212

• Artificial Intelligence • Previous Articles     Next Articles

Study of Crowd Counting Algorithm of “Weak Supervision” Dense Scene Based on DeepNeural Network

LIU Yan, LEI Yin-jie, NING Qian   

  1. College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China
  • Received:2019-07-31 Online:2020-04-15 Published:2020-04-15
  • Contact: NING Qian,born in 1969,associate professor.Her main research interests include computer application and intelligent control.
  • About author:LIU Yan,born in 1995,postgraduate.His main research interests include deep learning and crowd counting.
  • Supported by:
    This work was supported by the Key Research and Development Program of Sichuan Province (2019YFG0409)

Abstract: At present,in the crowd counting task of dense scenes,the method of annotating true density is to annotate the central position of pedestrian’s head.Gaussian convolution is used to generate the ground-truth density map as the supervision information.However,for dense scenes,such labeling method is time-consuming and laborious,and there are many “uncontrolled” factors in the images of dense scenes,such as low resolution,background noise,heavy occlusion and scale change.To solve this problem,we proposed a new annotation method,that is,we only need to know how many persons are included in the picture,and the total count of pedestrians in the picture is used as the supervision information.Compared with the traditional real density map,in proposed labeling method,the real target value is used as the “weak supervision” information.The experimental results show that the model obtained by training neural network with weak supervisory information can accurately regress the number of targets in the image for crowd regression task,indicating the effectiveness of this method.

Key words: Crowd counting, Deep learning, Neural network, Weak supervision

CLC Number: 

  • TP391.413
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