用个性化联邦学习提升监控视频暴力检测准确率
Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos
- 在联邦学习框架中引入个性化层，适应各监控节点数据差异
- 在平衡与非平衡数据上均达99.3%准确率，显著优于传统方法
- 适合隐私敏感的智慧城市监控系统，兼顾效率与安全
城市监控系统中的暴力事件检测面临视频数据量大且类型多样的挑战。本文提出一种基于个性化联邦学习（PFL）的针对性方法，采用Flower框架中的带个性化层的联邦学习机制。该方法使模型适配各监控节点的独特数据特征，有效应对监控视频数据异构、非独立同分布（non-IID）的问题。在平衡与非平衡数据集上的严格实验表明，PFL模型在准确率和效率方面均有提升，最高达到99.3%。本研究证实，PFL能显著增强监控系统的可扩展性与有效性，为复杂城市环境下的暴力检测提供一种强大且保护隐私的解决方案。
原文摘要 · Abstract (English)
The challenge of detecting violent incidents in urban surveillance systems is compounded by the voluminous and diverse nature of video data. This paper presents a targeted approach using Personalized Federated Learning (PFL) to address these issues, specifically employing the Federated Learning with Personalization Layers method within the Flower framework. Our methodology adapts learning models to the unique data characteristics of each surveillance node, effectively managing the heterogeneous and non-IID nature of surveillance video data. Through rigorous experiments conducted on balanced and imbalanced datasets, our PFL models demonstrated enhanced accuracy and efficiency, achieving up to 99.3% accuracy. This study underscores the potential of PFL to significantly improve the scalability and effectiveness of surveillance systems, offering a robust, privacy-preserving solution for violence detection in complex urban environments.
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