Towards Real-Time Monitoring of the Hajj

Muhammad Baqui, Rainald Löhner


An automated approach to explore the fundamental properties of high-density pedestrian traffic is outlined. The framework operates on video or time lapse images captured from surveillance cameras. For pedestrian velocity extraction, the framework incorporates cross-correlation based Particle Image Velocimetry (PIV) techniques. For pedestrian density estimation, the framework relies on the Machine Learning technique of the Boosted Regression Trees. The information collected from images in pixel coordinates are transformed to world coordinates with a pin-hole camera based projective transformation technique. The framework has been tested with high density crowd images acquired during the Muslim religious event, the Hajj. Accuracy and performance of the framework are reported.


crowd monitoring; particle-image velocimetry; machine learning; hajj

Full Text:



S. Almukhtar and D. Watkins, “How One of the Deadliest Hajj Accidents Unfolded,” The New York Times, 05-Sep-2016.

R. J. Adrian and J. Westerweel, Particle Image Velocimetry. Cambridge University Press 558, 2010.

B. Maurin, O. Masoud, and N. P. Papanikolopoulos, “Tracking all traffic: computer vision algorithms for monitoring vehicles, individuals, and crowds,” IEEE Robot. Autom. Mag., vol. 12, no. 1, pp. 29–36, Mar. 2005.

S. Nedevschi, S. Bota, and C. Tomiuc, “Stereo-Based Pedestrian Detection for Collision-Avoidance Applications,” IEEE Trans. Intell. Transp. Syst., vol. 10, no. 3, pp. 380–391, Sep. 2009.

Z. Ma and A. B. Chan, “Crossing the Line: Crowd Counting by Integer Programming with Local Features,” IEEE Conf. Comput. Vis. Pattern Recognit., vol. 1063–69/13, pp. 2535–2546, 2013.

H. Idrees, I. Saleemi, C. Seibert, and M. Shah, “Multi-source multi-scale counting in extremely dense crowd images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2547–2554.

B. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” Proc. Int. Jt. Conf. Artif. Intell., pp. 674–679, 1981.

R. J. Adrian and C. S. Yao, “Development of Pulsed Laser Velocimetry (PLV) For Measurement of Turbulent Flow,” in In Symposium on Turbulence. X.B. Reed Jr., G.K. Patterson ed, 1984, pp. 170– 184.

S. Vanlanduit, J. Vanherzeele, R. Longo, and P. Guillaume, “A digital image correlation method for fatigue test experiments,” Opt. Lasers Eng., vol. 47, no. 3, pp. 371–378, 2009.

J. K. Sveen and A. E. Cowen, “Quantitative Imaging Techniques and Their Application to Wavy Flows, In PIV and Water Waves,” World Sci., 2004.

M. Rossi, E. Esposito, and E. P. Tomasini, “PIV Application to Fluid Dynamics of Bass Reflex Ports,” in Particle Image Velocimetry, Springer, 2007, pp. 259–270.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, vol. 1, pp. 886–893.

J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Ann. Stat., pp. 1189–1232, 2001.

M. Baqui, “Automated Monitoring of High Density Crowd Events,” 2018.

P. Dollár, Piotr’s image and video Matlab Toolbox (PMT), 2013.

V. M. Predtechenskii and A. I. Milinskii, Planning for foot traffic flow in buildings. National Bureau of Standards, US Department of Commerce, and the National Science Foundation, Washington, DC, 1978.

R. Löhner, “On the Modeling of Pedestrian Motion,” Appl Math Model., vol. 34, no. 2, pp. 366– 382, 2010.


Copyright (c) 2020 Muhammad Baqui, Rainald Löhner

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.