A stereovision based approach for detecting and tracking lane and forward obstacles on mobile devices

Andra Petrovai, R. Danescu, S. Nedevschi
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引用次数: 19

Abstract

This paper presents SmartCoDrive, an Android application which performs driving assistance functions: 3D lane detection and tracking, forward obstacle detection, obstacle tracking. With this mobile application we wish to increase the adoption rate of driving assistance systems and to provide a viable and cheap solution for every driver, that will be able to use his own tablet or smartphone as a personal driving assistant. The mobile application is deployed on a tablet equipped with dual back-facing cameras. The visual information from the two cameras, along with the data received from the Controller Area Network bus of the vehicle enable a thorough understanding of the 3D environment. First, we develop the sparse 3D reconstruction algorithm. Then, using monocular vision we perform lane markings detection. Obstacle detection is done by combining the superpixel segmentation with 3D information and the tracking algorithm is based on the Kalman Filter. Since the processing capabilities of the mobile platforms are limited, different optimizations are carried out in order to obtain a real-time implementation. The Android application may be used in urban traffic that is characterized by low-speed and short-medium distances to obstacles.
一种基于立体视觉的方法,用于检测和跟踪移动设备上的车道和前方障碍物
本文介绍了一款具有驾驶辅助功能的Android应用程序SmartCoDrive,该应用程序具有三维车道检测与跟踪、前向障碍物检测、障碍物跟踪等功能。通过这个移动应用程序,我们希望提高驾驶辅助系统的采用率,并为每个司机提供一个可行且廉价的解决方案,让他们能够使用自己的平板电脑或智能手机作为个人驾驶助手。这款移动应用安装在配有双后置摄像头的平板电脑上。来自两个摄像头的视觉信息,以及从车辆控制器区域网络总线接收的数据,使人们能够彻底了解3D环境。首先,提出了稀疏三维重建算法。然后,使用单目视觉进行车道标记检测。障碍物检测采用超像素分割与三维信息相结合的方法,跟踪算法基于卡尔曼滤波。由于移动平台的处理能力有限,为了获得实时实现,进行了不同的优化。Android应用程序可用于低速和中短距离障碍物的城市交通。
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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