轻量化模型+时序分析，高效识别监控中的暴力行为
Vi-SAFE: A Spatial-Temporal Framework for Efficient Violence Detection in Public Surveillance
- 用优化的YOLOv8定位人体，再用时序网络判断暴力动作
- 在RWF-2000数据集上准确率达88%，比纯时序模型高11个百分点
- 适合需要实时响应的公共安全监控场景
公共场所暴力检测对公共安全至关重要。本文针对小目标、复杂环境及实时时序分析等挑战，提出Vi-SAFE空间-时序框架，融合改进的YOLOv8与时间片段网络（TSN）。YOLOv8采用GhostNetV3轻量骨干、指数移动平均注意力机制和剪枝技术，在保持精度的同时降低计算开销。模型分别在行人与暴力数据集上独立训练：YOLOv8提取人体区域，TSN完成暴力行为二分类。在RWF-2000数据集上的实验表明，Vi-SAFE准确率达到0.88，显著优于单独使用TSN（0.77），且在准确率与效率上均优于现有方法，验证了其在公共安全监控中的有效性。代码已公开。
原文摘要 · Abstract (English)
Violence detection in public surveillance is critical for public safety. This study addresses challenges such as small-scale targets, complex environments, and real-time temporal analysis. We propose Vi-SAFE, a spatial-temporal framework that integrates an enhanced YOLOv8 with a Temporal Segment Network (TSN) for video surveillance. The YOLOv8 model is optimized with GhostNetV3 as a lightweight backbone, an exponential moving average (EMA) attention mechanism, and pruning to reduce computational cost while maintaining accuracy. YOLOv8 and TSN are trained separately on pedestrian and violence datasets, where YOLOv8 extracts human regions and TSN performs binary classification of violent behavior. Experiments on the RWF-2000 dataset show that Vi-SAFE achieves an accuracy of 0.88, surpassing TSN alone (0.77) and outperforming existing methods in both accuracy and efficiency, demonstrating its effectiveness for public safety surveillance. Code is available at https://anonymous.4open.science/r/Vi-SAFE-3B42/README.md.
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