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相关概念视频

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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在大视频数据中用于异常事件识别的转移学习模型.

Roqaia Adel Taha1, Aliaa Abdel-Halim Youssif2, Mohamed Mostafa Fouad2

  • 1College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Smart Village, Cairo, Egypt. rokaiaadel2020@gmail.com.

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这项研究引入了用于自动监控的语义关键提取算法,大大减少了视频数据. 视觉变压器 (ViT_b16) 模型在识别异常事件方面实现了95.87%的准确性.

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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:

  • 视频监控系统在准确识别异常的人类活动方面面临挑战.
  • 高昂的监控成本和操作员疲劳限制了传统的CCTV系统的有效性.
  • 自动实时事件识别对于提高安全性至关重要.

研究的目的:

  • 开发一个语义关键提取算法,以有效地识别监控视频中的异常事件.
  • 使用这种新的方法来评估深度学习模型 (ResNet50,VGG19,EfficientNetB7,ViT_b16) 的性能.
  • 通过最小化处理要求来解决大量视频数据量的问题.

主要方法:

  • 提出了一个基于动作识别的语义关键提取算法.
  • 该算法与ResNet50,VGG19,EfficientNetB7和视觉转换器 (ViT_b16) 模型进行了集成.
  • 这些模型在UCF-Crime数据集上进行了训练和测试,包括正常和异常监控视频.

主要成果:

  • EfficientNetB7的准确率达到86.34%,VGG19的准确率达到87.90%,ResNet50的准确率达到90.46%.
  • 视觉变压器 (ViT_b16) 模型表现出卓越的性能,准确率为95.87%.
  • 与最先进的模型相比,拟议的方法显著改善了异常事件识别.

结论:

  • 语义关键提取算法有效地减少视频数据,同时保持异常检测的高精度.
  • 视觉变压器 (ViT_b16) 模型在监控中实时识别异常事件方面显示出非常有前途的潜力.
  • 这项研究为自动化安全系统提供了可扩展和准确的解决方案.