This paper presents a railway track and turnout detection algorithm which is not based on an empirical threshold. The railway track extraction is based on an edge detection using the width of the rolling pads. This edge detection scheme is then used as an input to the RANSAC algorithm to determine the model of the rails. The turnout detection scheme is based on the Histogram of Oriented Gradient (HOG) and Template Matching (TM). The results show (i) reliable performance for our railway track extraction scheme and (ii) a correction rate of 97.31 percent for the turnout detection scheme using a Support Vector Machine (SVM) classifier.


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    Title :

    Rail and turnout detection using gradient information and template matching


    Contributors:


    Publication date :

    2013-08-01


    Size :

    1212229 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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