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

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Proceedings of the Future Technologies Conference (FTC) 2023, Volume 3 (FTC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 815))

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Abstract

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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Notes

  1. 1.

     Throughout this paper, the DWL means one segment of the dashed white line; In contrast, the DWLs (plural) represent the overall dashed white line. Same rule applies to LAR as an uncountable noun. Thus, DWLs include numbers of DWL and numbers of LAR.

  2. 2.

     For the simplicity of repeatedly addressing, the term SVR is used only as the acronym of Same Vehicle Recognition in this paper, albeit otherwise commonly referred to as Support Vector Regression. Readers are hereby advised.

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Authors and Affiliations

  1. The University of Alabama, Tuscaloosa, AL, 35487, USA

    Chen Wang & Travis Atkison

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  1. Chen Wang
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  2. Travis Atkison
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Correspondence to Chen Wang .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Wang, C., Atkison, T. (2023). Mathematical Treatment for Real-Time Vehicle Recognition Using Traditional Road Surveillance Cameras. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2023, Volume 3. FTC 2023. Lecture Notes in Networks and Systems, vol 815. Springer, Cham. https://doi.org/10.1007/978-3-031-47457-6_8

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