Fatigue Detection Using Raspberry Pi 3

  • Authors

    • Akalya Chellappa
    • Mandi Sushmanth Reddy
    • R Ezhilarasie
    • S Kanimozhi Suguna
    • A Umamakeswari
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.11993
  • Driver drowsiness detection, Raspberry Pi 3, Raspbian camera, OpenCV, Feature Extraction, Eye Aspect Ratio (EAR).

  • Abstract

    Driver drowsiness is a primary cause of several highway calamities leads to severe physical injuries, loss of money, and loss of human life. The implementation of driver drowsiness detection in real-time will aid in avoiding major accidents. The system is designed for four-wheelers wherein the driver’s fatigue or drowsiness is detected and alerts the person. The proposed method will use 5-megapixel Raspbian camera that captures driver’s face and eyes and processes the images to detect driver’s fatigue. On the detection of drowsiness, the programmed system cautions the driver through an alarm to ensure vigilance. The proposed method constitutes of various stages to determine wakefulness of the driver. According to this output, the warning message is generated. Haar Cascade Classifiers is used to detect the blink duration of the driver and Eye Aspect Ratio (EAR) is calculated. Finally, the alert message along with car plate number is sent to the concerned person mobile with help of Ubidots cloud service and Twilio API. For this Raspberry Pi 3 with Raspbian (Linux Based) Operating System is used.

     

     

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  • How to Cite

    Chellappa, A., Sushmanth Reddy, M., Ezhilarasie, R., Kanimozhi Suguna, S., & Umamakeswari, A. (2018). Fatigue Detection Using Raspberry Pi 3. International Journal of Engineering & Technology, 7(2.24), 29-32. https://doi.org/10.14419/ijet.v7i2.24.11993

    Received date: 2018-04-24

    Accepted date: 2018-04-24

    Published date: 2018-04-25