This paper presents a new approach for road lane classification using an onboard camera. Initially, lane boundaries are detected using a linear-parabolic lane model, and an automatic on-the-fly camera calibration procedure is applied. Then, an adaptive smoothing scheme is applied to reduce noise while keeping close edges separated, and pairs of local maxima-minima of the gradient are used as cues to identify lane markings. Finally, a Bayesian classifier based on mixtures of Gaussians is applied to classify the lane markings present at each frame of a video sequence as dashed, solid, dashed solid, solid dashed, or double solid. Experimental results indicate an overall accuracy of over 96% using a variety of video sequences acquired with different devices and resolutions.


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    Title :

    Automatic Detection and Classification of Road Lane Markings Using Onboard Vehicular Cameras




    Publication date :

    2015




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.84 Straßenverkehr / 55.24 / 55.84 / 55.24 Fahrzeugführung, Fahrtechnik




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