Advertisement

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)

Abstract

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.

Keywords

Crowd counting Density classification Deep learning 

Notes

Acknowledgments

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).

References

  1. 1.
    Marsden, M., McGuiness, K., Little, S., O’Connor, N.E.: Fully convolutional crowd counting on highly congested scenes. arXiv preprint arXiv:1612.00220 (2016)
  2. 2.
    Zeng, L., Xu, X., Cai, B., Qiu, S., Zhang, T.: Multi-scale convolutional neural networks for crowd counting. arXiv preprint arXiv:1702.02359 (2017)
  3. 3.
    Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
  4. 4.
    Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 833–841. IEEE (2015)Google Scholar
  5. 5.
    Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 589–597. IEEE (2016)Google Scholar
  6. 6.
    Oñoro-Rubio, D., López-Sastre, R.J.: Towards perspective-free object counting with deep learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 615–629. Springer, Cham (2016). doi: 10.1007/978-3-319-46478-7_38 Google Scholar
  7. 7.
    Boominathan, L., Kruthiventi, S.S., Babu, R.V.: CrowdNet: a deep convolutional network for dense crowd counting. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 640–644. ACM (2016)Google Scholar
  8. 8.
    Shang, C., Ai, H., Bai, B.: End-to-end crowd counting via joint learning local and global count. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1215–1219. IEEE (2016)Google Scholar
  9. 9.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9. IEEE (2015)Google Scholar
  10. 10.
    Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2547–2554. IEEE (2013)Google Scholar
  11. 11.
    Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in Neural Information Processing Systems, pp. 1324–1332 (2010)Google Scholar
  12. 12.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  13. 13.
    Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning.4,2 (2012)Google Scholar

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

Personalised recommendations