Video surveillance on mobile edge networks - A reinforcement-learning-based approach

Haoji Hu, Hangguan Shan, Chuankun Wang, Tengxu Sun, Xiaojian Zhen, Kunpeng Yang, Lu Yu, Zhaoyang Zhang, Tony Q.S. Quek

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)


Video surveillance systems or Internet of Multimedia Things are playing a more and more important role in our daily life. To obtain useful surveillance information timely and accurately, not only image recognition algorithms but also computing and communication resources can be bottlenecks of the whole system. In this article, taking face recognition application as an example, we study how to build video surveillance systems by utilizing mobile edge computing (MEC), one of the 5G's key technologies. Specifically, to achieve high recognition accuracy and low recognition time, we design image recognition algorithms for both the camera sensor and MEC server, and utilize the action-value methods to train actions of the system by jointly optimizing offloading decision and image compression parameters. The experimental results show the advantages of the proposed system for enabling communication environment-adaptive, efficient, and intelligent video surveillance.

頁(從 - 到)4746-4760
期刊IEEE Internet of Things Journal
出版狀態Published - 2020 六月

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 資訊系統
  • 硬體和架構
  • 電腦科學應用
  • 電腦網路與通信


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