CNN-ViT 支持弱监控的视频段级异常检测
Md Haidar Sharif1, Lei Jiao1, Christian W Omlin1
1Department of ICT, University of Agder, 4630 Kristiansand, Norway.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究介绍了CNN-ViT-TSAN,一种新的弱监督视频异常事件检测 (WVAED) 方法. 它有效地提取使用CNN和视觉变压器 (ViT) 模型的特征,提高异常检测性能.
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:
- 视频异常事件检测 (VAED) 对于智能监控至关重要.
- 深度学习已经推进了VAED,弱监督的VAED (WVAED) 获得了引力.
- 目前的WVAED方法严重依赖于预训练的特征提取器.
研究的目的:
- 开发一种强大的WVAED方法,利用各种预训练的特征提取器.
- 为了有效地捕捉视频数据中的远程和短程时间依赖.
- 为WVAED提出一个通用的架构,集成多种特征提取技术.
主要方法:
- 使用预训练的卷积神经网络 (CNN) 模型 (C3D,I3D) 和视觉转换器 (ViT) 模型 (CLIP) 来进行特征提取.
- 引入了一个时间自我注意网络 (TSAN) 来建模时间依赖.
- 设计了一个基于多个实例学习 (MIL) 的架构,CNN-ViT-TSAN,集成CNN/ViT功能和TSAN.
主要成果:
- 拟议的CNN-ViT-TSAN架构在WVAED中证明了它的有效性.
- 该方法成功地使用组合特征提取器提取了精确的表示.
- 在人群数据集上的实验结果验证了该方法的性能.
结论:
- 在CNN-ViT-TSAN架构为弱监督的视频异常事件检测提供了一个有希望的方法.
- 整合各种特征提取器和时间注意力机制可以提高WVAED的性能.
- 拟议的方法为应对WVAED挑战提供了一个通用的框架.
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