Emergency vehicles such as ambulances and fire engines get stuck in a heavy traffic may lead to the loss of valuable lives. To overcome this problem, a framework is proposed to detect emergency vehicle in heavy traffic, using Region-Based Convolutional Neural Network (RCNN) to take quick decisions. When an ambulance comes in a particular direction in a traffic signal, the controller detects and checks for the density of traffic and speed of the vehicle in a particular direction and also calculates the time taken by the emergency vehicle to cross the road. Based on the information received, the controller alerts the alternate roads, by displaying a red signal for a particular time in its path. Once the emergency vehicle passes away from the signal, the master control has to be reset for maintaining the normal traffic flow.


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    Title :

    Emergency Vehicle Detection in Traffic Surveillance Using Region-Based Convolutional Neural Networks


    Additional title:

    Lect. Notes Electrical Eng.



    Conference:

    International Conference on Automation, Signal Processing, Instrumentation and Control ; 2020 February 27, 2020 - February 28, 2020



    Publication date :

    2021-03-05


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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