The paper introduces an intelligent system that utilizes machine learning to effectively detect traffic infractions. It incorporates adaptive stop line detection, robust licence plate extraction, and real-time traffic light monitoring. The system’s machine learning component accurately recognizes traffic signal colors, facilitating better traffic flow management. Adaptive stop line detection adjusts to various traffic situations, enhancing system flexibility. Licence plates are accurately extracted using Optical Character Recognition (OCR), which is powered by image processing and Haar Cascade classifiers. The system’s effectiveness in identifying traffic violations, especially during red lights, is demonstrated through video data testing. Additionally, the integration of a MySQL database enables comprehensive logging and tracking of penalized licence plates, providing a detailed overview of traffic infractions and keeping track of all the violating vehicles. This technological integration promises significant improvements in traffic enforcement and road safety. The combination of adaptive stop line detection, real-time traffic light analysis, and licence plate identification makes this system an invaluable tool for traffic control authorities. By improving enforcement and monitoring, the system has the potential to revolutionize traffic management and enhance safety measures. This proposed work suggests that such an intelligent system can be a sophisticated asset in addressing traffic-related challenges and ensuring safer road conditions.


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    Titel :

    Advanced Traffic Violation Detection with Tesseract OCR and Computer Vision


    Beteiligte:
    Gnanakumar, D (Autor:in) / Jairus Ponrabi, S V (Autor:in) / Ravindran, N (Autor:in) / Razvi, Ahmed (Autor:in) / Gokulakannan, K (Autor:in)


    Erscheinungsdatum :

    28.06.2024


    Format / Umfang :

    597447 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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