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Road vanishing point detection using weber adaptive local filter and salient-block-wise weighted soft voting

Road vanishing point detection using weber adaptive local filter and salient-block-wise weighted soft voting

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In this study, a novel and efficient technique is proposed for road vanishing point detection in challenging scenes. Currently, most existing texture-based methods detect the vanishing point using pixel wise texture orientation estimation and voting map generation, which suffers from high computational complexity. Since only road trails (e.g. road edges, ruts, and tire tracks) would contribute informative votes to vanishing point detection, the Weber adaptive local filter is proposed to distinguish road trails from background noise, which is envisioned to reduce the workload and to eliminate uninformative votes introduced by the background noise. Furthermore, instead of using the conventional pixel-wise voting scheme, the salient-block-wise weighted soft voting is developed to eliminate most of the noise votes introduced by incorrectly estimated pixel-wise texture orientations, and to further reduce the computation time of voting stage as well. The experimental results on the benchmark dataset demonstrate that the proposed method shows superior performance. The authors’ method is about ten times faster in detection speed and outperforms by 3.6% in detection accuracy, when compared with a well-known state-of-the-art approach.

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