Towards Real-Time Monitoring of the Hajj

Muhammad Baqui,Rainald Löhner

doi:10.17815/cd.2020.75

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.

Highlights

Every year millions of Muslims congregate to perform the Hajj
The framework relies on the Particle Image Velocimetry (PIV) [2] technique for pedestrian velocity extraction and a trained Machine Learning model for obtaining density
After obtaining velocity and density in the image/pixel coordinates, these are transformed to physical units in world coordinates through projective geometry

Summary

Introduction

Every year millions of Muslims congregate to perform the Hajj. Managing pedestrian safety and comfort for crowds of this size presents formidable challenges. Since surveillance cameras are widely used, studying video or time lapse photos from these cameras may provide valuable insights on the dynamics of high-density pedestrian traffic. The current work provides ways for obtaining velocity and density information from surveillance camera images. The framework relies on the Particle Image Velocimetry (PIV) [2] technique for pedestrian velocity extraction and a trained Machine Learning model for obtaining density. Image processing and computer vision techniques have been used to analyze various aspects of pedestrian dynamics, namely walking behavior, crowd monitoring, head counting, trajectory extraction etc. Maurin et al [3] have constructed a crowd monitoring system based on optical flow, segmentation, and Kalman filter. In order to obtain pedestrian density from a given image, the first step would be to get a headcount of the people in that image. Idrees et al [6] have constructed a Support Vector Regressor that has been trained with more than one feature (image gradients, Fourier peaks and interest point based samplings)

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