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Large-Scale Dataset for Radio Frequency-Based Device-Free Crowd Estimation

Author

Listed:
  • Abdil Kaya

    (IDLab—Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Stijn Denis

    (IDLab—Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Ben Bellekens

    (IDLab—Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Maarten Weyn

    (IDLab—Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

  • Rafael Berkvens

    (IDLab—Faculty of Applied Engineering, University of Antwerp—imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium)

Abstract

Organisers of events attracting many people have the important task to ensure the safety of the crowd on their venue premises. Measuring the size of the crowd is a critical first step, but often challenging because of occlusions, noise and the dynamics of the crowd. We have been working on a passive Radio Frequency (RF) sensing technique for crowd size estimation, and we now present three datasets of measurements collected at the Tomorrowland music festival in environments containing thousands of people. All datasets have reference data, either based on payment transactions or an access control system, and we provide an example analysis script. We hope that future analyses can lead to an added value for crowd safety experts.

Suggested Citation

  • Abdil Kaya & Stijn Denis & Ben Bellekens & Maarten Weyn & Rafael Berkvens, 2020. "Large-Scale Dataset for Radio Frequency-Based Device-Free Crowd Estimation," Data, MDPI, vol. 5(2), pages 1-11, June.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:2:p:52-:d:369172
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