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Accurate and Robust Vanishing Point Detection Method in Unstructured Road Scenes

机译:非结构化道路场景中的准确和稳健的消失点检测方法

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Vanishing point detection is an essential component of vision-based autonomous navigation for unmanned ground vehicles and mobile robots. In this paper, we propose an accurate and robust vanishing point detection method for unstructured road scenes, where the road scenes lack clear road markings and include complex background interference. Since only the road region provides informative clues for vanishing point detection, we first introduce the manifold ranking method to estimate the road region based on background suppression. Then, we develop a series of principles for voter selection, and propose a dynamic adjustment strategy for the candidate selection that reduces the search scope of the vanishing point to perform candidate selection. Finally, we propose an effective voting strategy, in which the candidate that achieves the greatest number of votes in the voting space is considered to be the vanishing point. The experimental results on a large number of unstructured road images show that our proposed method is more accurate and robust than five existing methods.
机译:消失点检测是无人机地面车辆和移动机器人的视觉基础自主导航的重要组成部分。在本文中,我们提出了一种对非结构化道路场景的准确和稳健的消失点检测方法,道路场景缺乏清晰的道路标记,包括复杂的背景干扰。由于只有道路区域提供了用于消失点检测的信息线索,因此我们首先介绍了基于背景抑制的道路区域来介绍歧管排名方法。然后,我们为选民选择制定一系列原则,并提出了候选选择的动态调整策略,从而减少了消失点的搜索范围来执行候选选择。最后,我们提出了一项有效的投票策略,其中候选投票空间中最多的投票的候选人被认为是消失点。在大量非结构化路面图像上的实验结果表明,我们所提出的方法比五种现有方法更准确且鲁棒。

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