IDG-ViolenceNet:一个视频暴力检测模型,集成身份意识图形和3D-CNN
1School of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究介绍了IDG-ViolenceNet,这是一种用于视频暴力检测的新型双流模型. 它通过将身份识别图形与3D-CNNs集成,以加强监控,在复杂场景中显著提高准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 智能监控和公共安全依赖于有效的视频暴力检测.
- 现有的方法难以在视频中建模复杂的多人互动.
研究的目的:
- 提出IDG-ViolenceNet,一个用于高级视频暴力检测的双流模型.
- 加强复杂的人际交互的建模,以提高公共安全.
主要方法:
- 开发了一个双流模型,将身份意识的时空图形与3D-CNN集成在一起.
- 利用YOLOv11进行精确的人身检测和身份追踪.
- 构建动态时空图,编码空间,时间和身份信息.
主要成果:
- 实现了高精度:97.5% (冰球战),99.5% (电影战) 和89.4% (RWF-2000).
- 在暴力检测方面显著超过现有的最先进的方法.
- 废弃性研究证实了单个模型组件的有效性.
结论:
- IDG-ViolenceNet为视频暴力检测提供了一个强大而准确的解决方案.
- 识别图形和3D-CNNs的整合有效地捕捉了复杂的交互.
- 该模型显示了现实世界智能监控应用的巨大潜力.
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