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Efficient and Deployable Violence Detection using Transfer Learning Framework for Public Safety

Authors

  • Sanam Narejo

    Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Pakistan
    Author

  • Shabana Hajano

    Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Pakistan
    Author

  • Anoud Shaikh

    Department of Software Engineering, Mehran University of Engineering and Technology, Pakistan
    Author

  • Jibran Virk

    Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Pakistan
    Author

  • Maheen Shaikh

    Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Pakistan
    Author

DOI:

https://doi.org/10.22581/muet1982.0125

Keywords:

Smart city , Violence detection , Transfer learning , Closed-Circuit Television , Convolution Neural Networks , Artificial Intelligence

Abstract

One growing concern in urban areas is public safety in gathering spaces. Traditional Closed-Circuit Television (CCTV) monitoring systems depend almost entirely on human operators, making them prone to error due to fatigue and distraction. Due to the growing number of violent events, it is essential to develop automated intelligent systems to facilitate quick and accurate detection of potential violence, specifically in public spaces.  This research aims to strengthen traditional surveillance systems by reducing human reliance in favour of AI-driven automation and implementing transfer learning (TL), a subset of Artificial Intelligence (AI), for developing an automated violence detection system. TL allows models trained on much larger, diverse datasets to be further fine-tuned for specific problems, e.g., violent actions in CCTV footage. Thus, the proposed system intends to efficiently classify violent and non-violent actions at a relatively lower computation cost. The final system is expected to be deployable in diverse public environments, thus contributing to faster incident response and improved public safety. 

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References

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Published

2025-10-28

Issue

Vol. 44 No. 4 (2025): October Issue

Section

Articles

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Copyright (c) 2025 Mehran University Research Journal of Engineering and Technology

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How to Cite

Efficient and Deployable Violence Detection using Transfer Learning Framework for Public Safety. (2025). Mehran University Research Journal of Engineering and Technology, 44(4), 01-16. https://doi.org/10.22581/muet1982.0125

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