Abstract

In an autonomous driving assistance system (ADAS), top views effectively represent objects around the vehicle on a 2D plane. Top-view images are therefore widely used to detect lines in ADAS applications such as lane-keeping assistance and parking assistance. Because line detection is a crucial step for these applications, the false positive detection of lines can lead to failure of the system. Specular reflections from a glossy surface are often the cause of false positives, and since certain specular patterns resemble actual lines in the top-view image, their presence induces false positive lines. Incorrect positions of the lines or parking stalls can thus be obtained. To alleviate this problem, we propose two methods to estimate specular pixels in the top-view image. The methods use a geometric property of the specular region: the shape of the specular region is stretched long in the direction of the camera as the distance between the camera and the light source becomes distant, resulting in a straight line. This property can be used to distinguish the specular region in images. One estimates the pixel-wise probability of the specularity using gradient vectors obtained from an edge detector and the other estimates specularity using the line equation of each line segment obtained by line detection. To evaluate the performance of the proposed method, we added our methods as a pre-processing step to existing parking stall detection methods and investigated changes in their performance. The proposed methods improved line detection performance by accurately estimating specular components in the top-view images.

Highlights

  • We chose this domain because the location of the camera in the top-view image can be obtained in generating the top-view image, and the specular regions are overstated in top-view images and can significantly hinder the line detection

  • We proposed specularity estimation methods that can be used as a pre-processing step in line detection applications using top-view images

  • The geometric property of specularity was used to overcome the limitation of the color-based approach, with which white lines and specular regions cannot be distinguished

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Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Perspective geometry of a camera causes planar surfaces such as a parking-lot floor to distort into peculiar shapes. Inverse perspective mapping [1] is a method to correct these irregularities that have been caused by factors such as lens distortion, and imaging geometry by employing an appropriate nonlinear mathematical model. Top-view images, known as a bird’s-eye view images, generated using the inverse perspective mapping have been widely used in autonomous driving assistance system (ADAS) applications

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