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Lightweight mobile network for real-time violence recognition.

Youshan Zhang1, Yong Li2, Shaozhe Guo1

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This study introduces MobileNet-TSM, a lightweight model for efficient violence recognition on mobile devices. It significantly reduces model size while maintaining high accuracy for real-time applications.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Existing violence recognition methods are computationally expensive and complex.
  • Large-scale deployment of violence recognition on mobile devices is challenging due to resource constraints.

Purpose of the Study:

  • To develop a lightweight violence recognition model suitable for mobile intelligent terminals.
  • To reduce computational complexity and model size for practical applications.

Main Methods:

  • Proposed MobileNet-TSM, a lightweight network based on MobileNet-V2.
  • Incorporated Temporal Shift Modules (TSM) to enhance extraction of dynamic inter-frame characteristics.
  • Extensive experiments were conducted on public datasets.

Main Results:

  • The MobileNet-TSM model has only 8.49MB parameters and an estimated total size of 175.86MB.
  • Achieved high accuracy: 97.959% (Crowd Violence), 97.5% (Hockey Fights), and 87.75% (RWF-2000).
  • Demonstrated a slight accuracy decrease (approx. 3%) compared to larger models, enabling mobile deployment.

Conclusions:

  • MobileNet-TSM offers an effective solution for real-time violence recognition on mobile devices.
  • The model's reduced size and computational cost facilitate practical deployment.
  • A real-time violence recognition application was successfully developed for Android terminals.