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Real-time threat assessment based on hidden Markov models.

Ourania Theodosiadou1, Despoina Chatzakou1, Theodora Tsikrika1

  • 1Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.

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Summary

This study introduces a hidden Markov model for real-time threat assessment in public events. The framework uses probabilistic methods to dynamically track threat levels, aiding security decisions.

Keywords:
hidden Markov modelshidden threat levelthreat assessmentvisual analysis

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Area of Science:

  • Security Studies
  • Artificial Intelligence
  • Computer Vision

Background:

  • Timely detection of threats is crucial for public safety and critical infrastructure security.
  • Effective threat assessment mechanisms are vital for law enforcement to prevent crime and terrorism.
  • Existing methods may not adequately capture the dynamic nature of evolving threats.

Purpose of the Study:

  • To propose a hidden Markov model-based framework for efficient and effective threat assessment.
  • To enable dynamic estimation of threat levels over time, considering past observations.
  • To support security personnel in making informed decisions for precautionary measures.

Main Methods:

  • Utilizing a probabilistic approach based on hidden Markov models (HMMs).
  • Estimating the threat level at discrete time points.
  • Incorporating the dynamic evolution of threats by considering historical data.
  • Handling noisy data through the probabilistic nature of the model.

Main Results:

  • The proposed HMM framework effectively assesses threat levels in real-time.
  • The model dynamically reflects the evolution of threats, allowing for early intervention.
  • The probabilistic approach accommodates noisy data, enhancing robustness.
  • Demonstrated applicability in identifying potential threats at public events using surveillance footage analysis.

Conclusions:

  • The hidden Markov model framework provides an effective tool for dynamic threat assessment.
  • This approach enhances security decision-making by providing timely and adaptive threat level estimations.
  • The framework shows promise for improving security at public events through automated analysis of surveillance data.