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.
Automatic Detection and Classification of Road Lane Markings Using Onboard Vehicular Cameras
IEEE Transactions on Intelligent Transportation Systems ; 16 , 6 ; 3160-3169
2015-12-01
2361620 byte
Article (Journal)
Electronic Resource
English
Automatic Detection and Classification of Road Lane Markings Using Onboard Vehicular Cameras
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