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Title	Gdlavid-Graph-Based Deep Learning Approach For Automatic Violence Detection In Videos
ID_Doc	27746
Authors	Vinitha G.; Narayana G.; Prasad P.V.H.; Mounika G.; Tamilselvi R.; Raghu; Srikanth B.; Kumar K.K.
Year	2025
Published	International Journal of Basic and Applied Sciences, 14, 2
DOI	http://dx.doi.org/10.14419/139d5v03
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. © Vinitha G. et al.
Author Keywords	Anomaly Detection; Deep Learning; Graph Neural Networks; Surveillance; Video Analysis; Violence Detection

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