With the increased number of vehicles on road, the number of road accidents is increasing day by day. Every year, approximately 1.35 million people die in those accidents. In most of those accidents, people can be saved if taken to the hospital at the earliest, but due to lack of information regarding the time and place of the accident it may not be possible. This paper aims at building a smart black box system, which can help in reducing the road accidents and increasing the chances of saving injured people’s lives through accident detection using MEMS accelerometer and thereby alerting the nearest hospital service and the police control room with the exact location of the accident with the help of GPS Sensor and Twilio SMS service. This smart wireless black box comes with an intelligent facial recognition software, which prevents vehicle theft by alerting the owner of the vehicle with the image of the unauthorized person captured through camera. There is also a built-in pre-accident detection mechanism in this black box, which prevents drunk and thereby stopping the vehicle’s motor and alerting the nearest police control room and the driver’s relatives. This black box system captures various environmental parameters through sensors from the surroundings such as temperature, humidity, obstacle distance, pulse of the driver, location coordinates of the vehicle and uploads all the data to the cloud and also provides the visualization of the data gathered through the sensors. If any parameter value exceeds the limit then this black box system alerts the corresponding authority via SMS.


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    Titel :

    Smart Wireless Black Box with Intelligent Facial Recognition System for Prevention of Accidents and Theft of Vehicles Using Raspberry Pi Along with Sensors Based on IoT


    Beteiligte:
    Kiran Mai, C. (Herausgeber:in) / Kiranmayee, B. V. (Herausgeber:in) / Favorskaya, Margarita N. (Herausgeber:in) / Chandra Satapathy, Suresh (Herausgeber:in) / Raju, K. Srujan (Herausgeber:in) / Varma, V. Brijendra (Autor:in) / Kiranmayee, B. V. (Autor:in) / Reddy, L. Ashwik (Autor:in) / Kumar, S. Sravan (Autor:in) / Varma, P. Sandeep (Autor:in)

    Erscheinungsdatum :

    2021-01-01


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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