基于二维s色空间的鲁棒道路线颜色识别

Jin Yan, Seung-Hae Baek, Soon-Yong Park
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引用次数: 1

摘要

本文提出了一种光照不变的车道颜色识别方法。传统的车道颜色识别方法大多受到光照变化的影响。在过去,通常使用HSV色彩空间来区分白色和黄色的道路线,因为HSV色彩空间是一个特定颜色的范围。然而,由于道路照明不是静态的,而是动态的,因此很难利用HSV空间进行准确的道路线识别。本文通过引入二维s色空间,提出了一种鲁棒的道路线颜色识别方法。白色和黄色特征聚类在二维s色空间中。在s空间中连续跟踪特征样本的质心,实现实时车道跟踪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust road line color recognition based on 2-dimensional S-color space
In this paper, we propose an illumination invariant lane color recognition method. Most of the conventional lane color recognition methods suffer from various illumination changes. In the past, the HSV color space has been commonly used to tell white and the yellow road lines, because the HSV color space is a range of specific colors. However, it is known that accurate road line recognition is difficult using the HSV space, because the road illumination is not static but always dynamic. In this paper, we propose a robust road line color recognition method by introducing a 2-dimensional S-color space. The white and yellow color features are clustered in the 2-D S-color space. The centroid of the feature samples in S-space is tracked continuously for real-time lane tracking.
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