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Regular Paper

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

Ecole Supérieure d’Informatique Electronique Automatique (ESIEA), 75005 Paris, France
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Abstract

This paper presents an original investigation into the domain of violence detection in videos, introducing an innovative approach tailored to the unique challenges of a federated learning environment. The study encompasses a comprehensive exploration of machine learning techniques, leveraging spatio-temporal features extracted from benchmark video datasets. In a notable departure from conventional methodologies, we introduce a novel architecture, the “Diff Gated” network, designed to streamline preprocessing and training while simultaneously enhancing accuracy. Our exploration of advanced machine learning techniques, such as super-convergence and transfer learning, expands the horizons of federated learning, offering a broader range of practical applications. Moreover, our research introduces a method for seamlessly adapting centralized datasets to the federated learning context, bridging the gap between traditional machine learning and federated learning approaches. The outcome of this study is a remarkable advancement in the field of violence detection, with our federated learning model consistently outperforming state-of-the-art models, underscoring the transformative potential of our contributions. This work represents a significant step forward in the application of machine learning techniques to critical societal challenges.

Keywords

artificial intelligencefederated learningneural networkviolence detectionvideo analysis

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Journal of Computer Science and Technology
Volume 39 Issue 5,
September 2024
Pages 1029-1039
DOI: 10.1007/s11390-024-3702-7

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Pajon Q, Serre S, Wissocq H, et al. Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures. Journal of Computer Science and Technology, 2024, 39(5): 1029-1039. https://doi.org/10.1007/s11390-024-3702-7

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Received: 22 August 2023
Accepted: 27 June 2024
Published: 13 September 2024
© Institute of Computing Technology, Chinese Academy of Sciences 2024