sis 21(33): e9

Research Article

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

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  • @ARTICLE{10.4108/eai.21-10-2021.171596,
        author={Ali Khan and Khurram S. Khattak and Zawar H. Khan and T. A. Gulliver and Waheed Imran and Nasru Minallah},
        title={Internet-of-Video Things Based Real-Time Traffic Flow Characterization},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={8},
        number={33},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={10},
        keywords={Internet of Video Things (IoVT), Raspberry Pi (RPi), Video Streaming, Intelligent Transportation Systems (ITS), Camlytics},
        doi={10.4108/eai.21-10-2021.171596}
    }
    
  • Ali Khan
    Khurram S. Khattak
    Zawar H. Khan
    T. A. Gulliver
    Waheed Imran
    Nasru Minallah
    Year: 2021
    Internet-of-Video Things Based Real-Time Traffic Flow Characterization
    SIS
    EAI
    DOI: 10.4108/eai.21-10-2021.171596
Ali Khan1, Khurram S. Khattak1,*, Zawar H. Khan2, T. A. Gulliver2, Waheed Imran1, Nasru Minallah1
  • 1: National Center in Big Data and Cloud Computing, UET Peshawar, Pakistan
  • 2: Department of Electrical and Computer Engineering, University of Victoria, Canada
*Contact email: khurram.s.khattak@gmail.com

Abstract

Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome these limitations, a low-cost real-time Internet-of-Video-Things solution has been proposed. The sensor node (fabricated using Raspberry Pi 3B, Pi cameral and power bank) has the capability to
stream 2 Mbps MJPEG video of 640x480 resolution and 20 frames per second (fps). The Camlytics traffic analysis software installed on a Dell desktop is employed for traffic flow characterization. The proposed solution was field-tested with vehicle detection rate of 85.3%. The novelty of the proposed system is that in addition to vehicle count, it has the capability to measure speed, density, time headway, time-space diagram and trajectories. Obtained results can be employed for road network planning, designing and management.