A Robust Vision-based Lane Detection using RANSAC Algorithm
In a pursuit to reduce ever increasing road accidents by developing an advanced driver assistance system (ADAS), this paper proposes a vision-based lane detection algorithm. This paper incorporates a framework constituting of color space conversion, region of interest (ROI), adaptive histogram equal...
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| Veröffentlicht in: | 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT) S. 1 - 5 |
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| Sprache: | Englisch |
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IEEE
23.09.2022
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| Abstract | In a pursuit to reduce ever increasing road accidents by developing an advanced driver assistance system (ADAS), this paper proposes a vision-based lane detection algorithm. This paper incorporates a framework constituting of color space conversion, region of interest (ROI), adaptive histogram equalization, clustering of lane pixels, and RANdom SAmple Consensus (RANSAC) to develop a lane detection algorithm. The advantage of adaptive histogram equalization is to adjust the pixel intensity of Shadow and illumination regions in the road image using a contrast limit function. Further, clustering of a lane pixels is used to count and accumulate lane pixels above certain threshold. Finally, a RANSAC algorithm is applied to remove outliers and fit the lane lines model. The advantage of proposed framework is to detect the ego-lane and also all the lane boundaries in the image plane. Moreover, based on visual analysis, algorithm reveals a superior lane detection performance suitable for illumination variation, shadow, and lane variant width. |
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| AbstractList | In a pursuit to reduce ever increasing road accidents by developing an advanced driver assistance system (ADAS), this paper proposes a vision-based lane detection algorithm. This paper incorporates a framework constituting of color space conversion, region of interest (ROI), adaptive histogram equalization, clustering of lane pixels, and RANdom SAmple Consensus (RANSAC) to develop a lane detection algorithm. The advantage of adaptive histogram equalization is to adjust the pixel intensity of Shadow and illumination regions in the road image using a contrast limit function. Further, clustering of a lane pixels is used to count and accumulate lane pixels above certain threshold. Finally, a RANSAC algorithm is applied to remove outliers and fit the lane lines model. The advantage of proposed framework is to detect the ego-lane and also all the lane boundaries in the image plane. Moreover, based on visual analysis, algorithm reveals a superior lane detection performance suitable for illumination variation, shadow, and lane variant width. |
| Author | Sukumar, N. Sumathi, P. |
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| Snippet | In a pursuit to reduce ever increasing road accidents by developing an advanced driver assistance system (ADAS), this paper proposes a vision-based lane... |
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| SubjectTerms | Adaptive histogram equalization Advanced driver assistance system Clustering algorithms Histograms Image color analysis Lane detection Lighting Road images Roads Visualization |
| Title | A Robust Vision-based Lane Detection using RANSAC Algorithm |
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