Intelligent transportation systems require advanced vehicle tracking and surveillance tools, but hardware upgrades such as sensors, radars, and high-resolution cameras may be economically unfeasible in some regions. This paper introduces a mathematical approach for extracting traffic information using only traditional stationary single-lens traffic cameras. The method features vehicle recognition and tracking, speed estimation, traffic condition awareness, and abnormal behavior detection. Though machine learning is used for vehicle detection, information extraction, and analysis rely on mathematical deductions. The method’s effectiveness provides a viable solution for medium-to-small scale transportation systems, supplementing existing surveillance measures. Implementing this approach significantly improves real-time traffic surveillance efficiency and reduces infrastructure upgrade costs, offering a practical, cost-effective solution for enhancing transportation systems.


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    Titel :

    Mathematical Treatment for Real-Time Vehicle Recognition Using Traditional Road Surveillance Cameras


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Arai, Kohei (Herausgeber:in) / Wang, Chen (Autor:in) / Atkison, Travis (Autor:in)

    Kongress:

    Proceedings of the Future Technologies Conference ; 2023 ; Vancouver, BC, Canada October 19, 2023 - October 20, 2023



    Erscheinungsdatum :

    09.11.2023


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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