Authors:
Ilias Lazarou
;
Anastasios Kesidis
and
Andreas Tsatsaris
Affiliation:
Department of Surveying and Geoinformatics Engineering, University of West Attica, Athens, 12243, Greece
Keyword(s):
Crowd Panic Detection, Biometrics, Wearable Devices, Machine Learning, Real-Time Analysis, Emergency Response Systems, Geospatial Data.
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 reve
al 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.
(More)