Counting the Number of People in Crowd as a Part of Automatic Crowd Monitoring: A Combined Approach

  • Yashna BhartiEmail author
  • Ravi Saharan
  • Ashutosh Saxena
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 106)


This paper describes a new technique for counting the number of people in a crowd, as a part of automatic crowd monitoring. The technique involves combining the two domains of crowd size estimation, one is approximate crowd size estimation and the second is counting the exact number of people in the crowd. A simple technique based on image features is used to approximate the crowd size, depending on which crowd is divided into different classes and then a technique of exact crowd count suitable for the class of image is applied to get the number of people in the crowd. Combining the two techniques may increase the time complexity, but at the same time, there is a significant increase in accuracy, which is the primary concern. It would be useful for agencies involved in the security of the gathering to avoid crowd-related disaster and also for organizations which are responsible for giving data related to the number of people appearing in public events.


Crowd density People counting Feature extraction Human detection 



The crowd images used in research is taken from a site called crowd safety and risk analysis. The link is:


  1. 1.
    Helbing, D., Johansson, A., Al-Abideen, H.Z.: Dynamics of crowd disasters: an empirical study. Phys. Rev. E 75(4), 046109 (2007).
  2. 2.
    Soomaroo, L., Murray, V.: Disasters at mass gatherings: lessons from history. PLoS Curr. 4 (2012).
  3. 3.
    Marana, A.N., Velastin, S.A., Costa, L.F., Lotufo, R.A.: Estimation of Crowd Density Using Image Processing, pp. 11–11(1997).
  4. 4.
    Ma, W., Huang, L., Liu, C.: Advanced local binary pattern descriptors for crowd estimation. In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA’08, vol. 2. IEEE (2008).
  5. 5.
    Hu, Y., Chang, H., Nian, F., Wang, Y., Li, T.: Dense crowd counting from still images with convolutional neural networks. J. Vis. Commun. Image Represent. 38, 530–539 (2016). Scholar
  6. 6.
    Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 470–475. IEEE (2012).
  7. 7.
    Chauhan, V., Kumar, S., Singh, S.K.: Human count estimation in high density crowd images and videos. In: 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE (2016).
  8. 8.
    Yoon, Y., Gwak, J., Song, J.-I., Jeon, M.: Conditional marked point process-based crowd counting in sparsely and moderately crowded scenes. In: 2016 International Conference on Control, Automation and Information Sciences (ICCAIS), pp. 215–220. IEEE (2016).
  9. 9.
    Lin, S.-F., Chen, J.-Y., Chao, H.-X.: Estimation of number of people in crowded scenes using perspective transformation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 31(6), 645–654 (2001). Scholar
  10. 10.
    Karpagavalli, P., Ramprasad, A.V.: Estimating the density of the people and counting the number of people in a crowd environment for human safety. In: 2013 International Conference on Communications and Signal Processing (ICCSP). IEEE (2013)Google Scholar
  11. 11.
    Saqib, M., Khan, S.D., Blumenstein, M.: Texture-based feature mining for crowd density estimation: a study. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Central University of RajasthanAjmerIndia
  2. 2.CMR Technical Campus HyderabadHyderbadIndia

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