Empirical Evaluation of Crowds Using Automated Methods

  • Muhammad BaquiEmail author
  • Michelle Isenhour
  • Rainald Löhner
Conference paper


This work presents a novel framework for automated monitoring of high density crowds from closed circuit television (CCTV) image data. The framework obtains pedestrian velocities from particle image velocimetry (PIV) technique and densities from a boosted ferns machine learning model. A pinhole camera based perspective correction scheme is employed to convert the 2D pixel coordinates into 3D metric coordinates. The framework is trained with and tested against real-world event data from the Hajj.


  1. 1.
    Adrian, R.: Particle-imaging techniques for experimental fluid-mechanics. Annu. Rev. Fluid Mech. 23, 260–304 (1991)CrossRefGoogle Scholar
  2. 2.
    Baqui, M., Löhner, R.: Real-time crowd safety and comfort management from CCTV images. pp. 10223–10223–14 (2017).
  3. 3.
    Daamen, W.: Modelling passenger flows in public transport facilities. DUP Science Deflt, Delft (2004)Google Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005), vol. 1, pp. 886–893. IEEE, San Diego (2005)Google Scholar
  5. 5.
    Dambalmath, P., Muhammad, B., Haug, E., Löhner, R.: Fundamental diagrams for specific very high density crowds. In: Proc. Pedestrian and Evacuation Dynamics, pp. 6–11. University of Science and Technology Press, Hefei (2016)Google Scholar
  6. 6.
    Dollár, P.: Piotrs image and video matlab toolbox (PMT). (2013)
  7. 7.
    Elith, J., Leathwick, J.R., Hastie, T.: A working guide to boosted regression trees. J. Anim. Ecol. 77(4), 802–813 (2008)CrossRefGoogle Scholar
  8. 8.
    Hoogendoorn, S.P., Daamen, W., Bovy, P.H.: Extracting microscopic pedestrian characteristics from video data. In: Transportation Research Board Annual Meeting, Washington, pp. 1–15 (2003)Google Scholar
  9. 9.
    Johansson, A., Helbing, D., Al-Abideen, H.Z., Al-Bosta, S.: From crowd dynamics to crowd safety: a video-based analysis. Adv. Complex Syst. 11(04), 497–527 (2008)CrossRefGoogle Scholar
  10. 10.
    Johansson, A., Batty, M., Hayashi, K., Al Bar, O., Marcozzi, D., Memish, Z.A.: Crowd and environmental management during mass gatherings. Lancet Infect. Dis. 12(2), 150–156 (2012)CrossRefGoogle Scholar
  11. 11.
    Keane, R., Adrian, R.: Optimization of particle image velocimeters. Part I. Double pulsed systems. Meas. Sci. Technol. 11(1), 1202–1215 (1990)Google Scholar
  12. 12.
    Predtechenskii, V., Milinskii, A.: Planning for foot traffic flow in buildings. National Bureau of Standards, US Department of Commerce, and the National Science Foundation, Washington, DC (1978)Google Scholar
  13. 13.
    Schadschneider, A., Klingsch, W., Klüpfel, H., Seyfried, A.: Evacuation dynamics: empirical results, modeling and applications. Encycl. Complexity Syst. Sci. 3142–3176 (2008).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Muhammad Baqui
    • 1
    Email author
  • Michelle Isenhour
    • 2
  • Rainald Löhner
    • 1
  1. 1.George Mason UniversityFairfaxUSA
  2. 2.Naval Post Graduate SchoolMontereyUSA

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