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Fast fight detection.

Ismael Serrano Gracia1, Oscar Deniz Suarez1, Gloria Bueno Garcia1

  • 1Department of Systems Engineering and Automation, E.T.S.I. Industriales, Ciudad Real, Castilla-La Mancha, Spain.

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Summary

This study introduces a faster method for violence detection in videos using motion blob features. While slightly less accurate than existing approaches, it offers real-time applicability for surveillance and safety.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Action recognition research has primarily focused on simple actions, neglecting complex events like violence.
  • Violence detection is crucial for surveillance in sensitive environments (prisons, psychiatric centers) and mobile devices.
  • Existing methods like Bag-of-Words for fight detection are computationally expensive, limiting practical use.

Purpose of the Study:

  • To develop a computationally efficient method for detecting violent events in video sequences.
  • To address the limitations of high computational costs in current violence detection algorithms.
  • To enable real-time violence detection for practical applications.

Main Methods:

  • Extraction of features from motion blobs within video sequences.
  • Utilizing these motion blob features to classify sequences as either fight or non-fight events.
  • Developing a novel approach to discriminate aggressive behavior from normal actions.

Main Results:

  • The proposed method achieves significantly faster computation times compared to state-of-the-art techniques.
  • While accuracy is slightly lower than existing methods, the speed improvement makes it suitable for real-time applications.
  • Demonstrates the effectiveness of motion blob features for violence detection.

Conclusions:

  • The novel motion blob-based method provides a viable, real-time solution for violence detection.
  • The trade-off between accuracy and computational cost favors this method for practical surveillance scenarios.
  • Further research can explore optimizing accuracy while maintaining computational efficiency.