Fast and Robust Vanishing Point Detection Using Contourlet Texture Detector for Unstructured Road

Abstract

Fast and robust vision-based road detection in an unstructured environment is very challenging. In this paper, we focus on vanishing-point (VP) detection in unstructured roads and propose a response-modulated line-voting method based on a contourlet transform, followed by a voter selection process for VP detection. We first adopt the contourlet transform to estimate the dominant vector for each pixel, including orientation and its relevant response. The estimated dominant vector is then selected by a novel select function to retrieve approximately 40% of the pixels with a reliable dominant vector in the image to vote. Unlike previous methods, this method takes into account the magnitudes of response of the pixels to improve the efficiency of the voting process by suppressing possible interference by extreme and strong textures. The pixels are given a moderate response to vote. Finally, for situations where the road texture is likely to be selected as a criterion for voting by the line-voting scheme, we use this simple and fast scheme to vote for the VP. We conduct experiments on a public dataset of 1,003 different types of natural road images as well as on our own dataset of 400 such images. The results demonstrate that in our dataset, the proposed method is comparable to and outperforms the state-of-the-art methods.

Keywords

• Contourlet transform
• line-voting method
• reliable voter selection
• response-modulated
• unstructured road
• VP detection