Extracting Crowd Velocities at High Density

Muhammad Baqui; Rainald Löhner

doi:10.17815/CD.2020.110

Extracting Crowd Velocities at High Density

Authors

Muhammad Baqui CFD Center, George Mason University, Fairfax, USA
Rainald Löhner CFD Center, George Mason University, Fairfax, USA

DOI:

https://doi.org/10.17815/CD.2020.110

Keywords:

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

Abstract

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.

References

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University, 2018.

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Published

12.08.2020

How to Cite

Baqui, M., & Löhner, R. (2020). Extracting Crowd Velocities at High Density. Collective Dynamics, 5, 493–495. https://doi.org/10.17815/CD.2020.110

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Issue

Vol. 5 (2020)

Section

Proceedings of Pedestrian and Evacuation Dynamics 2018

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