Real-Time Monitoring of Crowd Panic Based on Biometric and Spatiotemporal Data

Ilias Lazarou, Anastasios Kesidis, Andreas Tsatsaris

2023

Abstract

Panic is one of the most important indicators when it comes to Emergency Response Systems (ERS). Until now, panic events of any cause tend to be treated in a local manner based on traditional methods such as visual surveillance technologies and community engagement systems. This paper aims to present an approach for crowd panic event detection that takes advantage of wearable devices tracking real-time biometric data that are combined with location information. The real-time biometric and spatiotemporal nature of the data in the proposed approach is spatially unrestricted and information is flawlessly transmitted right from the source of the event, the human body. First, a machine learning classifier is demonstrated that successfully detects whether a subject has developed panic or not, based on its biometric and spatiotemporal data. Second, a real-time analysis model is proposed that uses the geospatial information of the labeled subjects to expose hidden patterns that possibly reveal crowd panic. The experimental results demonstrate the applicability of the proposed method in detecting and visualizing in real-time areas where an event of abnormal crowd behavior occurs.

Paper Citation

in Harvard Style

Lazarou I., Kesidis A. and Tsatsaris A. (2023). Real-Time Monitoring of Crowd Panic Based on Biometric and Spatiotemporal Data. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 1021-1027. DOI: 10.5220/0011789900003417

in Bibtex Style

@conference{visapp23,
author={Ilias Lazarou and Anastasios Kesidis and Andreas Tsatsaris},
title={Real-Time Monitoring of Crowd Panic Based on Biometric and Spatiotemporal Data},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={1021-1027},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011789900003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Real-Time Monitoring of Crowd Panic Based on Biometric and Spatiotemporal Data
SN - 978-989-758-634-7
AU - Lazarou I.
AU - Kesidis A.
AU - Tsatsaris A.
PY - 2023
SP - 1021
EP - 1027
DO - 10.5220/0011789900003417
PB - SciTePress