In this article, a system for speed estimation of vehicles in road traffic is presented. Using a state-of-the-art Convolutional Neural Network (CNN) for object detection, vehicles are first recognized as objects in the image material captured by a monocular camera, e.g. a mobile phone. In order to prevent the fluctuations of the bounding boxes of the detected vehicles from affecting the calculated velocity, a subsequent computer vision step is performed where the license plate of each individual vehicle is recognized based on a canny edge detection algorithm and a rectangular bounding box is drawn around the license plate. Repeating this for each individual frame in the video image material and observing the change of size of the license plate, the velocity of the vehicle is estimated based on the intercept theorem.


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    Title :

    Speed Estimation of Vehicles Using Monocular Cameras


    Contributors:


    Publication date :

    2023-07-19


    Size :

    1167864 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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