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

Year: 2025

Type: article

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.

Source: International Journal of Basic and Applied Sciences

DOI: 10.14419/139d5v03

Authors: Vinitha G, Narayana, P. Venkata Hari Prasad, G. Mounika, R. Tamilselvi, Raghu, Srikanth B, K. Kranthi Kumar

Institutions: Chaitanya Bharathi Institute of Technology, Koneru Lakshmaiah Education Foundation, Bahrain Polytechnic, Vasireddy Venkatadri International Technological University

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: diamond

OpenAlex ID: W4411211802

FWCI:

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