Smart Traffic Monitoring System Using Computer Vision and Edge Computing

Guanxiong Liu, Hang Shi, Abbas Kiani, Abdallah Khreishah, Joyoung Lee, Nirwan Ansari, Chengjun Liu, Mustafa Mohammad Yousef

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Traffic management systems capture tremendous video data and leverage advances in video processing to detect and monitor traffic incidents. The collected data are traditionally forwarded to the traffic management center (TMC) for in-depth analysis and may thus exacerbate the network paths to the TMC. To alleviate such bottlenecks, we propose to utilize edge computing by equipping edge nodes that are close to cameras with computing resources (e.g., cloudlets). A cloudlet, with limited computing resources as compared to TMC, provides limited video processing capabilities. In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC. Our solution utilizes two algorithms for each task, one implemented at the edge and the other one at the TMC, which are designed with the consideration of different computing resources. While the TMC provides strong computation power, the video quality it receives depends on the underlying network conditions. On the other hand, the edge processes very high-quality video but with limited computing resources. Our model captures this trade-off. We evaluate the performance of the proposed two-tier model as well as the traffic monitoring algorithms via test-bed experiments under different weather as well as network conditions and show that our proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

Original languageEnglish (US)
Pages (from-to)12027-12038
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number8
StatePublished - Aug 1 2022

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Automotive Engineering
  • Computer Science Applications


  • Traffic monitoring
  • edge-computing
  • incidents detection
  • video analytic


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