This paper presents a computer vision-based driver activity detection system with a Raspberry Pi to analyze the driver's facial expressions, eye movements, and head position to detect signs of fatigue, distractions caused by noise, and alcohol consumption. The system is cost-effective and can be easily installed in any vehicle. The collected data are processed in real time to alert the driver and passengers if any dangerous behavior is detected. It can also send notifications to authorities if necessary. The system is also useful for monitoring professional drivers, such as truck and bus drivers. Driver fatigue is a significant cause of road accidents, and this system aims to enhance road safety by detecting potentially dangerous situations.


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    Titel :

    Enhancing Road Safety: A System for Detecting Driver Activity Using Raspberry Pi and Computer Vision Techniques with Alcohol and Noise Sensors


    Weitere Titelangaben:

    Algorithms for Intelligent Systems


    Beteiligte:
    Jacob, I. Jeena (Herausgeber:in) / Piramuthu, Selwyn (Herausgeber:in) / Falkowski-Gilski, Przemyslaw (Herausgeber:in) / Sudarsanam, P. (Autor:in) / Anand, R. (Autor:in) / Challa, Manoj (Autor:in)

    Kongress:

    International Conference on Data Intelligence and Cognitive Informatics ; 2023 ; Tirunelveli, India June 27, 2023 - June 28, 2023



    Erscheinungsdatum :

    2024-01-07


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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