Fast obstacle detection using U-disparity maps with stereo vision

F. Oniga, Ervin Sarkozi, S. Nedevschi
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引用次数: 2

Abstract

This paper introduces a computationally efficient, approach for obstacle detection in driving assistance applications, based on stereovision. The proposed approach involves three different steps aiming an increased quality of the results. The first step relies on the basics: obstacles correspond to peak regions in the u-disparity map. By applying the model of the stereo system, the peaks are detected with an adaptive threshold. The adaptive thresholding will calculate the accumulation of points required for an obstacle based on its distance (disparity) and will be related to the characteristics of the stereovision system. The second and third steps consist of refining the result of the previous, vertically respectively horizontally. This is necessary in order to fill out unmarked pixel regions which are classified as belonging to obstacles. The second step iterates vertically and propagates the obstacle label to neighbor pixels. The third step improves obstacle regions horizontally, with points that do not belong to the road. The solution is fast and reliable, on various scenarios, as every step is an improvement of the standard U-disparity approach for obstacle detection.
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快速障碍物检测使用u -视差地图与立体视觉
本文介绍了一种基于立体视觉的辅助驾驶障碍检测方法。提议的方法包括三个不同的步骤,旨在提高结果的质量。第一步依赖于基本原理:障碍物对应于u-视差图中的峰值区域。通过应用立体系统模型,采用自适应阈值检测峰值。自适应阈值将根据障碍物的距离(视差)计算障碍物所需的点数积累,并将与立体视觉系统的特性相关。第二步和第三步分别在垂直方向和水平方向对前一步的结果进行细化。这是必要的,以便填充归类为属于障碍物的未标记像素区域。第二步垂直迭代并将障碍物标签传播到相邻像素。第三步,用不属于道路的点水平地改善障碍区域。该解决方案在各种场景下都是快速可靠的,因为每一步都是对标准u -视差方法的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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