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

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8966998
Pages (from-to)4746-4760
Number of pages15
JournalIEEE Internet of Things Journal
Volume7
Issue number6
DOIs
Publication statusPublished - 2020 Jun

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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