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深度强化学习授权的成本效益高的联合视频监控管理框架

Dilshod Bazarov Ravshan Ugli1, Alaelddin F Y Mohammed2, Taeheum Na3

  • 1Department of Computing, Gachon University, Seongnam-si 13120, Republic of Korea.

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PubMed
概括
此摘要是机器生成的。

本研究介绍了一种高效的视频监控系统,使用边缘计算来优化深度学习模型的使用. 它动态管理GPU资源,减少对增强安全应用程序的计算需求.

关键词:
DQN DQN 在线观看这是LSTM的LSTM.有成本效益的视频监控管理系统.联合学习的联合学习层次化的边缘计算.

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科学领域:

  • 计算机视觉和机器学习
  • 边缘计算架构 边缘计算架构
  • 人工智能用于安全安全.

背景情况:

  • 深度学习 (DL) 提高了视频监控的精度,但需要大量的计算和内存资源 (GPU功率和内存).
  • 目前用于监控中管理DL模型的现有方法通常使用静态值或移动平均值,这些都是低效的.
  • 在视频监控系统中,对实时对象跟踪和行为分析的需求带来了大量的处理挑战.

研究的目的:

  • 引入一种新的视频监控管理系统,优化运营效率并降低计算需求.
  • 为了动态管理基于DL的视频监控服务的GPU使用和内存分配.
  • 为了改善GPU内存保护和DL模型重新加载延迟之间的平衡.

主要方法:

  • 实现一个双层边缘计算架构 (通过插座传输实现客户端-服务器).
  • 使用联合学习 (FL) 来训练长期短期记忆 (LSTM) 网络来预测对象的外观.
  • 采用深度Q网络 (DQN) 方法论来动态控制DL模型释放值,由LSTM预测提供信息.

主要成果:

  • 拟议的系统通过动态控制值模块有效减少不必要的GPU使用.
  • 主要边缘 (客户端) 的实时对象检测可以最大限度地降低数据传输延迟.
  • 基于DQN的动态值管理平衡了GPU内存保护与模型重新加载延迟.

结论:

  • 这种新系统显著提高了基于DL的视频监控中的计算资源的效率和有效使用.
  • 这种方法可以通过优化资源配置和性能,在各种领域提高安全性.
  • 使用DQN和LSTM预测的动态值管理为静态方法提供了优越的替代方案.