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arXiv · 2308.05106

Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures

Pajon Quentin·Serre Swan·Wissocq Hugo·Rabaud Léo·Haidar Siba·Yaacoub Antoun
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Abstract

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.

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Pajon Quentin, Serre Swan, Wissocq Hugo, Rabaud Léo, Haidar Siba, Yaacoub Antoun. 2023-06-29. Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures. https://doi.org/10.1007/s11390-024-3702-7

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