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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.

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|September 28, 2023
PubMed
概括
此摘要是机器生成的。

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.

关键词:
马哈拉诺比斯是距离的距离关注注意力注意力注意力注意力卷积神经网络 (CNN) 是一种神经网络.多个实例学习 (MIL)视觉变压器 (ViT) 是一个视觉变压器.弱监督的视频异常事件检测事件检测.

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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.