In autonomous driving, road markings are an essential element for high-precision mapping, trajectory planning and can provide important information for localization. This paper presents an approach to detect, classify and approximate a great variety of road markings using a stereoscopic camera system. We present an algorithm that is able to classify characters and arrows as well as stop-lines, pedestrian crossings, dashed and straight lines, etc. The classification is independent of orientation, position or the exact shape. This is achieved using a histogram of the marking width as main part of the feature vector for line-shaped markings and Optical Character Recognition (OCR) for characters. Classification is done by an Artificial Neural Network (ANN). We have evaluated our approach over a 10.5 km drive through an urban area.


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    Title :

    A Universal Approach to Detect and Classify Road Surface Markings


    Contributors:


    Publication date :

    2015-09-01


    Size :

    1996512 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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