Managing Edge AI Cameras for Traffic Monitoring

Guan Wen Chen, Yi Hsiu Lin, Min Te Sun, Tsi Ui Ik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationAPNOMS 2022 - 23rd Asia-Pacific Network Operations and Management Symposium
Subtitle of host publicationData-Driven Intelligent Management in the Era of beyond 5G
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784885523397
DOIs
StatePublished - 2022
Event23rd Asia-Pacific Network Operations and Management Symposium, APNOMS 2022 - Takamatsu, Japan
Duration: 28 Sep 202230 Sep 2022

Publication series

NameAPNOMS 2022 - 23rd Asia-Pacific Network Operations and Management Symposium: Data-Driven Intelligent Management in the Era of beyond 5G

Conference

Conference23rd Asia-Pacific Network Operations and Management Symposium, APNOMS 2022
Country/TerritoryJapan
CityTakamatsu
Period28/09/2230/09/22

Keywords

  • AI cameras
  • embedded system
  • management system

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