Shadow detection and removal for illumination consistency on the road

Chunxiang Wang, Liuyuan Deng, Zhiyu Zhou, Ming Yang, Bing Wang
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引用次数: 15

Abstract

Shadows on the road always trouble vision tasks like visual navigation and road detection. Shadows will change road characteristics and occlude road objects. It is a great challenge to effectively detect and remove the shadows on the road to maintain illumination consistency for the vehicle. To tackle the adverse effect caused by shadows on the road, this paper attempts to detect shadows with Support Vector Machine (SVM) based on color saliency space and gradient field. Shadowed areas are distinguished and recognized by nonlinear SVM classifier through reconstructing road shadow descriptor after analyzing its color saliency space and gradient information. Then adaptive variable scale regional compensation operator is adopted to remove the shadows. Extensive experiments show that the shadow detection and removal method proposed in this paper has good feasibility and adaptability, and the method performs well under a variety of road environment.
道路照明一致性的阴影检测和去除
道路上的阴影总是给视觉导航和道路探测等视觉任务带来麻烦。阴影将改变道路特征并遮挡道路对象。如何有效地检测和去除道路上的阴影以保持车辆的照度一致性是一个巨大的挑战。为了解决阴影对道路的不利影响,本文尝试使用基于颜色显著性空间和梯度场的支持向量机(SVM)来检测阴影。通过分析道路阴影描述符的颜色显著性空间和梯度信息,重构道路阴影描述符,利用非线性支持向量机分类器对阴影区域进行区分和识别。然后采用自适应变尺度区域补偿算子去除阴影。大量实验表明,本文提出的阴影检测与去除方法具有良好的可行性和适应性,该方法在多种道路环境下均表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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