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相关实验视频

Updated: Jul 25, 2025

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基于目标检测的智能监视系统.

Jing Xue1, Wen Wu1, Qingkai Cheng1

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China.

Multimedia tools and applications
|June 26, 2023
PubMed
概括
此摘要是机器生成的。

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这项研究引入了一种智能远程监控系统,使用EfficientDet和中心点跟踪来检测在线考试期间的作弊. 该系统在识别可疑行为方面达到高准确度,并使用音频分析进行进一步的证据.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 教育技术的教育技术

背景情况:

  • 由于COVID-19大流行,学术考试和招聘面试需要转向在线远程监控.
  • 这种转变增加了学术不诚实和作弊的机会.
  • 现有的远程监控方法缺乏强大的机制来检测欺诈活动.

研究的目的:

  • 开发一个智能监控系统,能够检测和防止在线远程测试环境中的作弊.
  • 提高在线评估的完整性和可靠性.
  • 提供综合解决方案,将视觉和听觉分析结合起来,用于作弊检测.

主要方法:

  • 利用EfficientDet目标检测网络模型对受试者的行为进行视觉分析.
  • 集成了一个中心点跟踪算法,用于对个人的持续监控和跟踪.
  • 开发了一种辅助音频检测模块,用于分析可疑活动的环境声音.

主要成果:

  • EfficientDet网络实现了目标检测的平均检测准确率为81%.
  • 拟议的作弊行为检测模型在远程测试场景中展示了有效的检测,跟踪和识别.
  • 使用在线测试视频的现实实验显示,欺骗行为检测准确率为83.1%.
关键词:
检测异常行为 检测异常行为音频分析 音频分析中心神经位的跟踪.有效的检测智能监视 智能监视 智能监视

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结论:

  • 智能监控系统有效地解决了在线远程评估中作弊的挑战.
  • EfficientDet视觉检测和音频分析的结合提供了一种强大可靠的方法,以确保学术完整性.
  • 该系统为大学和企业提供了一种实用的解决方案,用于进行安全的在线考试和面试.