Managing Edge AI Cameras for Traffic Monitoring

DOI
  • Guan-Wen Chen
    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
  • Yi-Hsiu Lin
    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
  • Min-Te Sun
    Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
  • Tsì-Uí İk
    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

抄録

AI cameras are edge devices that can execute lightweight deep learning models with embedded GPU devices. In traffic management applications, traffic flow and traffic incidents can be detected from roadside images by AI cameras, and only the detected high-level information needs to be sent back to the server to avoid network bandwidth consumption and spare server resources. However, due to limited hardware resources at edge devices, the models should be optimized for specific AI cameras before they are deployed. In addition, the environment-related parameters need to be configured properly after model deployment. These tasks call for an AI camera management system. In this research, we design a management and deployment traffic monitoring system which can accomplish model optimization and parameter configuration with ease. Except for the camera hardware installation, other main functions can be called remotely from the management system, including 1) Automatic modeling and code transfer generation; 2) Remote deep learning model deployment; 3) Remote application configuration; 4) Analysis result presentation with a graphical user interface. To validate our proposed system, the embedded GPU devices, including NVIDIA Jetson TX2 and AGX Xavier combined with roadside cameras, are used as the prototype of the AI cameras, and the deployment of intersection flow analysis models and the visualized analysis results are conducted by the proposed system. The experiments validate that the proposed management system achieves all the design goals.

収録刊行物

  • IEICE Proceeding Series

    IEICE Proceeding Series 70 PS1-04-, 2022-09-28

    The Institute of Electronics, Information and Communication Engineers

詳細情報 詳細情報について

  • CRID
    1390012556785851008
  • DOI
    10.34385/proc.70.ps1-04
  • ISSN
    21885079
  • 本文言語コード
    en
  • データソース種別
    • JaLC
  • 抄録ライセンスフラグ
    使用不可

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