Towards Real-Time Monitoring of the Hajj

Muhammad Baqui; Rainald Löhner

doi:10.17815/CD.2020.75

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.2020.75

Keywords:

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

Abstract

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.

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