Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures
It addresses efficient violence detection for surveillance systems, though it appears incremental with architecture modifications and dataset adaptation.
This paper tackled violence detection in surveillance videos using federated learning, achieving better accuracy than state-of-the-art models by proposing a modified 'Diff-Gated' architecture and adapting centralized datasets.
This paper presents an investigation into machine learning techniques for violence detection in videos and their adaptation to a federated learning context. The study includes experiments with spatio-temporal features extracted from benchmark video datasets, comparison of different methods, and proposal of a modified version of the "Flow-Gated" architecture called "Diff-Gated." Additionally, various machine learning techniques, including super-convergence and transfer learning, are explored, and a method for adapting centralized datasets to a federated learning context is developed. The research achieves better accuracy results compared to state-of-the-art models by training the best violence detection model in a federated learning context.