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Vision-Based Lane Departure Detection Using Local Hough Transform and Particle Filter

机译：基于视觉霍夫变换和粒子滤波的基于视觉的车道偏离检测

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摘要

Lane-departure detection is an interesting and challenging task in the field of computer vision, providing an efficient way to improve driving safety. In this paper, we present a vision based lane-departure detection algorithm using local Hough transform and particle filter. A monocular vision based experiment platform also has been designed for implementing our algorithm. There are two main contributions in our work: Firstly, a novel lane detection method using local Hough transform was proposed to obtain the lane marking coordinates location. This method was proved to apply to fast and dynamic detection results. Secondly, we designed a detecting and tracking strategy, which is implemented by a particle filter, to ensure the stability of the system. The actual roads experiments are used to test the performance of our algorithm, and the experimental results shows that the proposed method is of demonstrated feasibility and reliability on actual roads.
机译：车道偏离检测在计算机视觉领域是一项有趣且具有挑战性的任务，它提供了一种提高驾驶安全性的有效方法。在本文中，我们提出了一种使用局部霍夫变换和粒子滤波的基于视觉的车道偏离检测算法。还设计了基于单眼视觉的实验平台来实现我们的算法。我们的工作有两个主要贡献：首先，提出了一种使用局部Hough变换的新型车道检测方法，以获取车道标记坐标位置。实践证明，该方法适用于快速，动态的检测结果。其次，我们设计了一种检测和跟踪策略，该策略由粒子过滤器实现，以确保系统的稳定性。通过实际道路实验对算法性能进行测试，实验结果表明，该方法在实际道路上具有可行性和可靠性。

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