开发基于游戏理论的超图的自动编码方案,用于监控视频的多个对象跟踪和异常检测
Palanivel Srinivasan1, Karthik Mohan2, Manivannan Doraipandian3
1Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, 201 204, India.
Scientific reports
|November 17, 2025
概括
这项研究引入了监控中异常检测的统一框架,将游戏理论超图匹配 (GTHG) 与卷积自编码器 (CAE) 集成在一起. 这种新的方法提高了在对象跟踪期间识别可疑活动的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在监视中检测异常对于安全和保障至关重要.
- 基于图形和超图形 (HG) 的方法有助于对象跟踪以检测异常.
- 现有的方法难以在同时跟踪和检测中平衡准确性和效率.
研究的目的:
- 提出一个统一的框架,用于监控录像中检测异常.
- 将游戏理论超图匹配 (GTHG) 与卷积自编码器 (CAE) 集成在一起.
- 通过结合结构一致性和外观重建来提高检测准确性和计算性能.
主要方法:
- 开发了一个统一的框架,整合了GTHG和CAE.
- 结合结构一致性和外观重建,用于异常检测.
- 在基准视频数据集上测试了该方法,分析了对匹配.
主要成果:
- 实现了高的曲线下面积 (AUC) 分数:88.7% (UCSD Ped1),91.2% (UCSD Ped2) 和86.6% (CUHK大道).
- 与现有的异常检测模型相比,其表现优越.
- 验证了综合方法在提高准确性和效率方面的有效性.
结论:
- 拟议的GTHG和CAE综合框架在异常检测方面取得了重大进展.
- 这种统一的方法有效地解决了单独的跟踪和检测方法的局限性.
- 该框架为加强监控安全和安全标准提供了强有力的解决方案.
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