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

Khan, Ali and Khattak, Khurram S. and Khan, Zawar H. and Gulliver, T. and Imran, Waheed and Minallah, Nasru (2021) Internet-of-Video Things Based Real-Time Traffic Flow Characterization. EAI Endorsed Transactions on Scalable Information Systems, 8 (33). e9. ISSN 2032-9407

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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.

Item Type: Article
Uncontrolled Keywords: Internet of Video Things (IoVT), Raspberry Pi (RPi), Video Streaming, Intelligent Transportation Systems (ITS), Camlytics
Subjects: Q Science > Q Science (General)
Depositing User: EAI Editor IV
Date Deposited: 08 Nov 2021 07:20
Last Modified: 08 Nov 2021 07:20
URI: https://eprints.eudl.eu/id/eprint/8022

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