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http://dx.doi.org/10.9717/kmms.2017.20.2.144

Preprocessing Technique for Lane Detection Using Image Clustering and HSV Color Model  

Choi, Na-Rae (Dept. of Data Science, Dankook University)
Choi, Sang-Il (Dept. of Computer Science and Engineering, Dankook University)
Publication Information
Journal of Korea Multimedia Society / v.20, no.2, 2017 , pp. 144-152 More about this Journal
Abstract
Among the technologies for implementing autonomous vehicles, advanced driver assistance system is a key technology to support driver's safe driving. In the technology using the vision sensor having a high utility, various preprocessing methods are used prior to feature extraction for lane detection. However, in the existing methods, the unnecessary lane candidates such as cars, lawns, and road separator in the road area are false positive. In addition, there are cases where the lane candidate itself can not be extracted in the area under the overpass, the lane within the dark shadow, the center lane of yellow, and weak lane. In this paper, we propose an efficient preprocessing method using k-means clustering for image division and the HSV color model. When the proposed preprocessing method is applied, the true positive region is maximally maintained during the lane detection and many false positive regions are removed.
Keywords
Lane Detection; Preprocessing; Clustering; Adaptive Histogram; Color Model; False Positive;
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Times Cited By KSCI : 1  (Citation Analysis)
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