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.
Highlights
Automatic driving technology has been studied for many years
To improve the accuracy and time consumption of the previously discussed methods, we propose a texture-based response-modulated line-voting (RMLV) method to estimate the dominant vector by a contourlet transform followed by a voter selecting process, a response modulation process, and a linevoting scheme to implement fast and robust VP detection
VANISHING POINT DETECTION METHOD we describe our VP voting method, which consists of two steps: (1) response modulation of the dominant vector of detected pixels; and (2) VP voting
Summary
Automatic driving technology has been studied for many years. A vital part of it is detecting both well-paved roads and unstructured roads, which are usually in an area with many variations in color, illumination, texture, and weather conditions. To improve the accuracy and time consumption of the previously discussed methods, we propose a texture-based RMLV method to estimate the dominant vector by a contourlet transform followed by a voter selecting process, a response modulation process, and a linevoting scheme to implement fast and robust VP detection. CONTOURLET TEXTURE DETECTOR This section describes our CTD, which consists of two major steps: (1) estimation of the dominant vector, including dominant orientation and its relevant response; and (2) detection of pixels by the dominant vector. We used the contourlet transform to estimate the dominant texture vector at each pixel, including the dominant orientation and its relevant response. Considering the aliasing effect between subbands, a texture orientation number of the pixels through contourlet transform greater than 16 would not maintain a reliable estimation of each subband. We can use fewer pixels with different orientation as the voters, saving much time
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