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Efficient Human Violence Recognition for Surveillance in Real Time.

Herwin Alayn Huillcen Baca1, Flor de Luz Palomino Valdivia1, Juan Carlos Gutierrez Caceres2

  • 1Academic Department of Engineering and Information Technology, Professional School of Systems Engineering, Faculty of Engineering, Jose Maria Arguedas National University, Andahuaylas 03701, Peru.

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|January 26, 2024
PubMed
Summary

This study introduces an efficient real-time human violence recognition model for video surveillance. The proposed system effectively detects violence, outperforming existing methods on diverse datasets.

Keywords:
VioPeruglobal temporal extractorhuman violence recognitionreal timeshort temporal extractorspatial attentionspatial motion extractorvideo surveillance

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human violence recognition is crucial for public safety and surveillance.
  • Existing methods often prioritize precision over real-time efficiency.
  • There is a need for practical, efficient violence detection models.

Purpose of the Study:

  • To develop an effective and efficient model for real-time human violence recognition.
  • To address the limitations of existing precision-focused approaches.
  • To create a robust system applicable to real-world surveillance scenarios.

Main Methods:

  • A novel three-module model: Spatial Motion Extractor (SME), Short Temporal Extractor (STE), and Global Temporal Extractor (GTE).
  • SME extracts regions of interest, STE captures rapid movement dynamics, and GTE identifies long-term temporal features.
  • The model was evaluated for efficiency, effectiveness, and real-time performance.

Main Results:

  • The proposed model demonstrated high efficiency on Hockey, Movies, and RWF-2000 datasets.
  • Superior effectiveness was achieved on the newly created VioPeru dataset, specifically designed for real-world surveillance.
  • The model's real-time applicability was validated through rigorous testing.

Conclusions:

  • The developed model offers a significant advancement in efficient and effective real-time human violence recognition.
  • It provides a practical solution for video surveillance systems, enhancing public safety.
  • The VioPeru dataset serves as a valuable benchmark for future research in this domain.