This paper proposes a fast method for detecting symbolic road markings (SRMs) and stop-lines. The proposed method efficiently restricts the search area based on the lane detection results and finds SRMs and stop-lines in a cost-effective manner. The SRM detector generates multiple SRM candidates using a top-hat filter and projection histogram and classifies their types using a histogram of oriented gradient (HOG) feature and total error rate (TER)-based classifier. The stop-line detector creates stop-line candidates via random sample consensus (RANSAC)-based parallel line pair estimation and verifies them using the HOG feature and TER-based classifier. The proposed method achieves reasonable detection rates and extremely low false positive rates along with a fast computing time.


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    Title :

    Fast symbolic road marking and stop-line detection for vehicle localization


    Contributors:
    Suhr, Jae Kyu (author) / Jung, Ho Gi (author)


    Publication date :

    2015-06-01


    Size :

    983633 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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