The AMBER Alert system is utilized to disseminate information about missing children, with approximately 85% of cases involving vehicles. However, the current system relies on simple text-based information through SMS, which may benefit from enhancements to expedite the identification of the involved vehicles. This paper addresses this challenge by proposing a Tiny Machine Learning (TinyML) model that leverages vehicle images depicting various types and colors, as well as images of missing children, to optimize AMBER Alerts and increase the chances of rescuing abducted children and older adults. The model incorporates the OpenCV library to perform real-time identification of car models and colors, enabling the transmission of detailed vehicle descriptions, including accompanying images, to aid in matching with emergency alerts. The considered vehicle types encompass SUVs, sedans, trucks, buses, microbuses, minivans, and motorcycles, while vehicle colors are classified into eight categories. By utilizing small scale machine learning framework known as (TinyML) technology, the proposed model can be deployed on small, low-power devices, significantly enhancing the speed and efficiency of the AMBER Alert system. This approach aims to equip law enforcement with a robust tool for swiftly and safely locating and rescuing abducted individuals through improved vehicle type and color detection and classification, facilitated by machine learning and image recognition technologies.


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    Titel :

    Modified AMBER Alerts System Using TinyML Processing


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Arai, Kohei (Herausgeber:in) / Khan, Mohammed Umair (Autor:in) / Al Shamrani, Ahmed (Autor:in) / Al Shami, Ahmad (Autor:in)

    Kongress:

    Proceedings of the Future Technologies Conference ; 2023 ; Vancouver, BC, Canada October 19, 2023 - October 20, 2023



    Erscheinungsdatum :

    02.11.2023


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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