Empirical Evaluation of Crowds Using Automated Methods

Abstract

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.