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

• URL: http://arxiv.org/abs/2308.05106v1
• Date: Thu, 29 Jun 2023 19:44:02 GMT
• Title: Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures
• Authors: Pajon Quentin, Serre Swan, Wissocq Hugo, Rabaud L\'eo, Haidar Siba, Yaacoub Antoun
• Abstract summary: The study includes experiments with-temporal detection features extracted from benchmark video datasets. Various machine learning techniques, including super-convergence and transfer learning, are explored. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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