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

作　　者：Quentin Pajon Swan Serre Hugo Wissocq Léo Rabaud Siba Haidar Antoun Yaacoub 

机构地区：[1]Ecole Supérieure d’Informatique Electronique Automatique(ESIEA),75005 Paris,France

出　　处：《Journal of Computer Science & Technology》2024年第5期1029-1039,共11页计算机科学技术学报（英文版）

摘　　要：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.

关 键 词：artificial intelligence federated learning neural network violence detection video analysis 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]