Applying multi level processing for robust geometric lane feature extraction

Lindner, Philipp; Blokzyl, Stephan; Wanielik, Gerd; Scheunert, U.

doi:10.1109/mfi.2010.5604446

Cited by 14 publications

(11 citation statements)

References 6 publications

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“…An experiment on the prototype HONGQI autonomous land vehicle is carried out by Chen et al According to Lindner et al [10] lane-mark features can be extracted and classified on the road based on the multilevel processing. They carried out real vehicle experiments on the prototype Carai, which provides a grey-scale image pre-processed by Canny edge detector.…”

Section: Research Based On Lane Trajectory Predictionmentioning

confidence: 99%

Survey on various lane and driver detection techniques based on image processing for hilly terrain

Manoharan

Daniel

2018

IET Image Processing

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Section: Research Based On Lane Trajectory Predictionmentioning

confidence: 99%

Survey on various lane and driver detection techniques based on image processing for hilly terrain

Manoharan

Daniel

2018

IET Image Processing

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“…Lindner et al [4] presented an approach to extract reliable lane markings using multi-level feature extraction and classification. The image data for the system is provided by the front camera of concept vehicle Carai.…”

Section: Related Workmentioning

confidence: 99%

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

Paula

Jung

2015

IEEE Trans. Intell. Transport. Syst.

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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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“…Schubert et al [15] studied lane changing based on road marker recognition surrounding traffic situation but the road markers classified were limited to only; dashed and solid lines. In a work by Lindner et al [16], an edge detector technique was designed to subsequently search for a group of four objects namely the lines, curves, parallel curves and closed objects in detecting road markers. Although four types of road markers were classified, most of the road markers are dashed lines with different sizes.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Video Processing using Contour Numbers and Angles for Non-urban Road Marker Classification

Sani¹,

Ghani²,

Besar³

et al. 2018

IJECE

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Road users make vital decisions to safely maneuver their vehicles based on the road markers, which need to be correctly classified. The road markers classification is significantly important especially for the autonomous car technology. The current problems of extensive processing time and relatively lower average accuracy when classifying up to five types of road markers are addressed in this paper. Two novel real time video processing methods are proposed by extracting two formulated features namely the contour number, , and angle, 𝜃 to classify the road markers. Initially, the camera position is calibrated to obtain the best Field of View (FOV) for identifying a customized Region of Interest (ROI). An adaptive smoothing algorithm is performed on the ROI before the contours of the road markers and the corresponding two features are determined. It is observed that the achievable accuracy of the proposed methods at several non-urban road scenarios is approximately 96% and the processing time per frame is significantly reduced when the video resolution increases as compared to that of the existing approach.

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Applying multi level processing for robust geometric lane feature extraction

Cited by 14 publications

References 6 publications

Survey on various lane and driver detection techniques based on image processing for hilly terrain

Survey on various lane and driver detection techniques based on image processing for hilly terrain

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

Real-Time Video Processing using Contour Numbers and Angles for Non-urban Road Marker Classification

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