Extracting Crowd Velocities at High Density

Muhammad Baqui, Rainald Löhner


Velocity is a fundamental property of foot traffic flow. Monitoring the change of velocity patterns at high pedestrian densities may provide valuable insights on foot traffic dynamics. In this paper, a closer look is taken to explore the capability of the Particle Image Velocimetry (PIV) technique on extracting crowd velocities from surveillance camera images. Experiments are performed to report the accuracy of pedestrian velocity extraction with PIV. Quantitative accuracy is reported with manual tracking of pedestrians, surveying correlation misses at different window sizes and compute times. The results indicate that the PIV algorithm can be a good candidate for velocity extraction in real-time.


particle image velocimetry (piv); crowd monitoring; pedestrian flow; surveillance

Full Text:



R. J. Adrian, “Particle-Imaging Techniques for Experimental Fluid-Mechanics,” Annu. Rev. Fluid

Mech., vol. 23, pp. 260–304, 1991.

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.

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.

J. Ma, W. Song, S. M. Lo, and Z. Fang, “New insights into turbulent pedestrian movement pattern in

crowd-quakes,” J. Stat. Mech. Theory Exp., vol. 2013, no. 02, p. P02028, 2013.

M. Baqui and R. Löhner, “Real-time crowd safety and comfort management from CCTV images,” in

Real-Time Image and Video Processing 2017, 2017, vol. 10223, p. 1022304.

M. Baqui, “Automated Monitoring of High Density Crowd Events,” PhD Thesis, George Mason

University, 2018.

DOI: http://dx.doi.org/10.17815/CD.2020.110

Copyright (c) 2020 Muhammad Baqui, Rainald Löhner

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