DeepGuard: Enhancing Violence Detection in Smart Cities Through Deep Learning - Md. Al-Mamun Provath; Musfequa Rahman; Kaushik Deb; Pranab Kumar Dhar; Tetsuya Shimamura (IEEE Access)
Video violence detection is essential to ensure public safety in smart cities, particularly in the context of political violence. Existing datasets and traditional methods for video violence detection often fall short of capturing the complexities of political violence, limiting the effectiveness of current models. To address these gaps, we propose a comprehensive approach that includes the development of a new dataset specifically focused on political violence, comprising 480 labeled video clips across four distinct categories. This dataset provides a broader understanding of political violence and is a benchmark for future research. We fine-tuned the Mobile Video Network (MoViNet-A0) to achieve an accuracy of 92.86% with 1.904M parameters. In addition, a custom keyframe extraction algorithm was designed, using temporal and pixel-level features to improve stability and accuracy. Our approach outperforms state-of-the-art methods, demonstrating high accuracy across multiple benchmark datasets. These results underscore the effectiveness of our system in detecting political violence, which offers significant implications for improving security measures and informing policy decision-making.