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基于注意力的双向长期短期记忆,用于检测异常的人类活动.

Manoj Kumar1,2, Anoop Kumar Patel2, Mantosh Biswas2

  • 1JSS Academy of Technical Education, Noida, India.

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|September 2, 2023
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概括
此摘要是机器生成的。

这项研究引入了一个深度学习框架,用于检测视频中的异常人类行为. 这种新的方法在对异常活动进行分类时达到很高的准确性,为监控和控制提供了一种实际的解决方案.

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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:

  • 异常的人类行为带来社会风险,包括攻击和在线仇恨犯罪.
  • 现有的监测和控制异常活动的方法往往是不切实际的或不可行的.
  • 深度学习从视频数据中对异常人类活动进行分类的潜力在很大程度上尚未被探索.

研究的目的:

  • 开发和评估一个深度学习框架,用于检测和分类在视频流中异常的人类行为.
  • 解决目前监测和控制与异常活动相关的社会风险的方法的局限性.

主要方法:

  • 实现了一种新的深度学习架构,结合了卷积神经网络 (CNN),双向长期短期记忆 (Bi-LSTM) 和注意力机制.
  • 该框架分析原始视频流的时空特征,以检测异常的人类活动.
  • 该模型旨在可靠地将检测到的异常行为分配到其特定的类别中.

主要成果:

  • 拟议的框架实现了跨多个数据集的高检测准确性.
  • 具体准确率包括UCF11的98.9%,UCF50的96.04%,UCF下犯罪数据集的61.04%.
  • 性能与最先进的算法进行了比较,证明了拟议架构的有效性.

结论:

  • 开发的深度学习方法显示了在视频中准确检测和分类异常人类行为的巨大潜力.
  • 该框架为监测和减轻异常活动造成的社会伤害提供了实用和可行的解决方案.
  • 进一步的研究可以探索这种模型在现实世界的场景中的应用,以提高公共安全和保障.