Improving Deep Crowd Density Estimation via Pre-classification of Density

  • Shunzhou Wang
  • Huailin Zhao
  • Weiren Wang
  • Huijun Di
  • Xueming Shu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)


Previous works about deep crowd density estimation usually chose one unified neural network to learn different densities. However, it is hard to train a compact neural network when the crowd density distribution is not uniform in the image. In order to get a compact network, a new method of pre-classification of density to improve the compactness of counting network is proposed in this paper. The method includes two networks: classification neural network and counting neural network. The classification neural network is used to classify crowd density into different classes and each class is fed to its corresponding counting neural networks for training and estimating. To evaluate our method effectively, the experiments are conducted on UCF_CC_50 dataset and Shanghaitech dataset. Comparing with other works, our method achieves a good performance.


Crowd counting Density classification Deep learning 



This work is supported by the National Natural Science Foundation of China (No. 9142020013), the National Natural Science Foundation of China (No. 71774094) and the National Science and Technology Pillar Program during the 12th Five-year Plan Period (No. 2015BAK12B03).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shunzhou Wang
    • 1
  • Huailin Zhao
    • 1
  • Weiren Wang
    • 2
  • Huijun Di
    • 3
  • Xueming Shu
    • 4
  1. 1.School of Electrical and Electronic EngineeringShanghai Institute of TechnologyShanghaiChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.School of Computer Science and Technology, Beijing Institute of TechnologyBeijingChina
  4. 4.Department of Engineering PhysicsTsinghua UniversityBeijingChina

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