This paper proposes a robust obstacle detection and recognition method for driver assistance systems. Unlike existing methods, our method aims to detect and recognize obstacles on the road rather than all the obstacles in the view. The proposed method involves two stages aiming at an increased quality of the results. The first stage is to locate the positions of obstacles on the road. In order to accurately locate the on-road obstacles, we propose an obstacle detection method based on the U-V disparity map generated from a stereo vision system. The proposed U-V disparity algorithm makes use of the V-disparity map that provides a good representation of the geometric content of the road region to extract the road features, and then detects the on-road obstacles using our proposed realistic U-disparity map that eliminates the foreshortening effects caused by the perspective projection of pinhole imaging. The proposed realistic U-disparity map greatly improves the detection accuracy of the distant obstacles compared with the conventional U-disparity map. Second, the detection results of our proposed U-V disparity algorithm are put into a context-aware Faster-RCNN that combines the interior and contextual features to improve the recognition accuracy of small and occluded obstacles. Specifically, we propose a context-aware module and apply it into the architecture of Faster-RCNN. The experimental results on two public datasets show that our proposed method achieves state-of-the-art performance under various driving conditions.

中文翻译：

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用于驾驶员辅助系统的稳健障碍物检测和识别

本文提出了一种用于驾驶员辅助系统的鲁棒障碍物检测和识别方法。与现有方法不同，我们的方法旨在检测和识别道路上的障碍物，而不是视野中的所有障碍物。所提出的方法涉及两个阶段，旨在提高结果的质量。第一阶段是定位道路上障碍物的位置。为了准确定位道路上的障碍物，我们提出了一种基于立体视觉系统生成的 UV 视差图的障碍物检测方法。提出的 UV 视差算法利用 V 视差图，它提供了道路区域几何内容的良好表示来提取道路特征，然后使用我们提出的真实 U 视差图检测道路障碍物，该图消除了针孔成像透视投影引起的透视效果。与传统的 U 视差图相比，所提出的真实 U 视差图大大提高了远处障碍物的检测精度。其次，将我们提出的 UV 视差算法的检测结果放入一个上下文感知的 Faster-RCNN 中，该 Faster-RCNN 结合了内部和上下文特征，以提高对小的和被遮挡的障碍物的识别精度。具体来说，我们提出了一个上下文感知模块并将其应用到 Faster-RCNN 的架构中。在两个公共数据集上的实验结果表明，我们提出的方法在各种驾驶条件下都达到了最先进的性能。