面向多尺度-多层次概率分析的自适应道路检测

Zhiyu Jiang, Qi Wang, Yuan Yuan
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引用次数: 7

摘要

基于视觉的道路检测是一个具有挑战性的问题,因为道路的形状多变,光照多变。虽然在这个问题上做了很多努力,但取得的成绩远远不能令人满意。为此,本文提出了一种贝叶斯方法,该方法同时探索被认为是互补的多尺度-多层次线索。该方法提出了两个贡献。1)利用新颖的拉普拉斯稀疏子空间聚类方法计算超像素级先验分布,利用统计颜色相似度计算像素级观测似然,有效推断道路区域的后验概率。2)为了保证道路模型在各种条件下的适应性,提出了一种多尺度策略,将不同尺度的检测结果融合在一起。在几个具有挑战性的视频序列上的实验结果验证了该方法与几种常用方法相比的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive road detection towards multiscale-multilevel probabilistic analysis
Vision-based road detection is a challenging problem because of the changeable shape and varying illumination. Though many efforts have been spent on this topic, the achieved performance is far from satisfactory. To this end, this paper formulates a Bayesian method which simultaneously explores the multiscale-multilevel clues that are considered to be complementary. Two contributions are claimed in this proposed method. 1) By computing the prior distribution in super-pixel-level with a novel Laplacian Sparse Subspace Clustering and observation likelihood in pixel-level with statistical color similarity, the posterior probability of road region can be effectively inferred. 2) To ensure the adaptivity of road model in various conditions, a multiscale strategy is presented to fuse the detection results of different scales. Experimental results on several challenging video sequences verify the superiority of the proposed method compared with several popular ones.
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