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  3. Real-time Violence Detection And Localization Through Subgroup Analysis.
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  3. Real-time Violence Detection And Localization Through Subgroup Analysis.

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Real-time violence detection and localization through subgroup analysis.

Emmeke Veltmeijer1, Morris Franken2, Charlotte Gerritsen1

  • 1Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, the Netherlands.

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|February 17, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new method for early Violence Detection (VD) by tracking subgroups, improving social awareness and localization of violent events in surveillance footage. The approach enhances existing models for real-time intervention.

Keywords:
LocalizationSubgroup analysisSurveillance dataViolence detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Surveillance Systems

Background:

  • Existing violence detection (VD) methods struggle with real-world surveillance data, lacking localization and social context.
  • Timely human intervention is critical, but current VD systems are often inadequate for complex scenarios.

Purpose of the Study:

  • To develop an adaptable add-on module for VD that integrates subgroup recognition and tracking.
  • To enhance real-time VD systems with improved social awareness and localization of violent incidents.

Main Methods:

  • Proposed a novel approach to integrate subgroup tracking into existing VD models.
  • Developed a system to recognize and track multiple subgroups across video frames.
  • Integrated localization capabilities to identify groups involved in violent events.

Main Results:

  • The method improves social awareness in real-time VD by localizing individuals involved in violence.
  • Achieved 91.3% accuracy on the SCFD dataset and 87.2% on the RWF-2000 dataset.
  • Demonstrated practical utility and generalization to unseen datasets, performing close to state-of-the-art.

Conclusions:

  • The proposed subgroup integration enhances VD systems, providing crucial localization and social dimension information.
  • This adaptable module offers a promising advance for early violence detection in surveillance.
  • The method's efficiency and generalization capabilities highlight its practical applicability in real-world scenarios.