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access icon free Vanishing point detection using random forest and patch-wise weighted soft voting

© The Institution of Engineering and Technology
Received 02/02/2016, Accepted 17/06/2016, Revised 20/05/2016, Published 20/06/2016

Variations in road types and its ambient environment make the single image based vanishing point detection a challenging task. In this study, a novel and efficient vanishing point detection method is proposed by using random forest and patch-wise weighted soft voting. To eliminate the noise votes introduced by background region and to reduce the workload of voting stage, random forest based valid patch extraction technique is developed, which distinguishes the informative road patches from the background noise. To prepare training data for the random forest, a training patch generation method is proposed, and a variety of road relevant features are introduced for training patch representation. Since the traditional pixel-wise voting scheme is time consuming and imprecise, a patch-wise weighted soft voting scheme is proposed to generate a more precise voting map and to further reduce the computational complexity of voting stage. The experimental results on the benchmark dataset show that the proposed method reveals a step forward in performance. The authors’ approach is about 6 times faster in detection speed and 5.6% better in detection accuracy than the generalised Laplacian of Gaussian filter based method, which is a well-known state-of-the-art approach.

Inspec keywords: Gaussian processes; traffic engineering computing; image representation; roads; image filtering; feature extraction; computational complexity

Other keywords: background region; patch-wise weighted soft voting scheme; single image based vanishing point detection; voting map; road types; training patch generation method; noise votes; informative road patches; random forest; computational complexity; training patch representation; generalised Laplacian; background noise; valid patch extraction technique; Gaussian filter

Subjects: Traffic engineering computing; Other topics in statistics; Image recognition; Other topics in statistics; Computer vision and image processing techniques; Filtering methods in signal processing

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