Traffic management has become one of the most complicated issues of recent times in metropolitan cities. Conventional traffic signaling systems are pre-programmed and alternate between red and green lights without any estimation of traffic. This signaling methodology leads to problems during peak hours at the intersections, where traffic ratio in a few lanes are dense when compared to others. Therefore, an efficient model is needed, which can manage the traffic flow at a certain point. The proposed model offers a solution using the CCTV footage from signal cameras to decongest traffic, based on a live estimate of traffic density. A state-of-the-art Deep Neural Network algorithm determines the number of vehicles and their type at a particular signal for object detection called You Only Look Once (YOLO), as it provided speed and accuracy in real-time. Based on vehicle count and road associated parameters, traffic density is computed to provide a dynamic extension of signaling time for a particular lane. Therefore, time saved from empty lanes is used to clear traffic on other busy lanes.


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    Titel :

    Vision-Based Automated Traffic Signaling


    Weitere Titelangaben:

    Advs in Intelligent Syst., Computing


    Beteiligte:
    Pant, Millie (Herausgeber:in) / Kumar Sharma, Tarun (Herausgeber:in) / Arya, Rajeev (Herausgeber:in) / Sahana, B.C. (Herausgeber:in) / Zolfagharinia, Hossein (Herausgeber:in) / Mallika, H. (Autor:in) / Vishruth, Y. S. (Autor:in) / Venkat Sai Krishna, T. (Autor:in) / Biradar, Sujay (Autor:in)


    Erscheinungsdatum :

    2020-06-30


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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