GDLAVID-graph-based Deep Learning Approach for ‎Automatic Violence Detection in Videos

  • Authors

    • Vinitha G Assistant Professor, Department of Computer Science and Engineering, Karpaga Vinayaga College of Engineering and ‎Technology, Chengalpattu
    • Narayana Garlapati Narayana, Associate Professor, Dept. of CSE (AIML), Chaitanya Bharathi Institute of Technology, Gandipet-‎Hyderabad
    • P. Venkata Hari Prasad Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation ‎Vaddeswaram, Guntur - 522302
    • G. Mounika Assistant Professor, Department of Computer Science & Engineering, R.V.R & J.C College of Engineering, Guntur
    • R. Tamilselvi Assistant Professor, Department of Computer Science and Design, SNS College of Engineering, Coimbatore
    • Raghu Lecturer, Department of Computer Science & Engineering, Bahrain Polytechnic, Bahrain
    • Srikanth B Professor Department of Computer Science and Engineering, Kallam Haranadhareddy Institute of Technology, Guntur
    • K. Kranthi Kumar Professor Department of Information Technology, Vasireddy Venkatadri International Technological University, Namburu ‎Guntur
    https://doi.org/10.14419/139d5v03

    Received date: May 11, 2025

    Accepted date: June 5, 2025

    Published date: June 11, 2025

  • Violence Detection; Graph Neural Networks; Video Analysis; Surveillance; Deep Learning; Anomaly Detection

  • Abstract

    This paper presents a method for detecting violence in videos using Graph Neural Networks (GNNs) and Spatio-Temporal Graph Neural ‎Networks (ST-GNNs). In this approach, each video frame is turned into a graph where people and objects are treated as nodes, and their ‎interactions are represented by connections. By studying these interactions over time, violent activities can be identified. The method was ‎tested on the Smart-City CCTV Violence Detection Dataset for Automatic Violence Detection in Videos, from Kaggle, which contains short ‎video clips labeled as violent or non-violent. The results show that this technique is effective in recognizing violent incidents in different ‎situations, making it useful for public safety and real-time surveillance.

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  • How to Cite

    G, V. ., Narayana, Prasad , P. V. H. ., Mounika , G. ., Tamilselvi , R. ., Raghu, B, S. ., & Kumar , K. K. . (2025). GDLAVID-graph-based Deep Learning Approach for ‎Automatic Violence Detection in Videos. International Journal of Basic and Applied Sciences, 14(2), 169-175. https://doi.org/10.14419/139d5v03