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Stereo vision-based vehicle detection using a road feature and disparity histogram

机译：使用道路特征和视差直方图的基于立体视觉的车辆检测

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摘要

This paper presents stereo vision-based vehicle detection approach on the road using a road feature and disparity histogram. It is not easy to detect only vehicles robustly on the road in various traffic situations, for example, a nonflat road or a multiple-obstacle situation. This paper focuses on the improvement of vehicle detection performance in various real traffic situations. The approach consists of three steps, namely obstacle localization, obstacle segmentation, and vehicle verification. First, we extract a road feature from v-disparity maps binarized using the most frequent values in each row and column, and adopt the extracted road feature as an obstacle criterion in column detection. However, many obstacles still coexist in each localized obstacle area. Thus, we divide the localized obstacle area into multiple obstacles using a disparity histogram and remerge the divided obstacles using four criteria parameters, namely the obstacle size, distance, and angle between the divided obstacles, and the difference of disparity values. Finally, we verify the vehicles using a depth map and gray image to improve the performance. We verify the performance of our proposed method by conducting experiments in various real traffic situations. The average recall rate of vehicle detection is 95.5%.
机译：本文提出了一种使用道路特征和视差直方图的基于立体视觉的道路车辆检测方法。在各种交通情况下，例如非平坦道路或多障碍物情况下，仅鲁棒地检测道路上的车辆并不容易。本文着重于在各种实际交通情况下提高车辆检测性能。该方法包括三个步骤，即障碍物定位，障碍物分割和车辆验证。首先，我们从使用每行和每列中最频繁的值进行二值化的v视差图中提取道路特征，并将提取的道路特征用作列检测中的障碍标准。但是，在每个局部障碍物区域中仍然存在许多障碍物。因此，我们使用视差直方图将局部障碍物区域划分为多个障碍物，并使用四个标准参数重新合并划分的障碍物，即障碍物大小，距离和障碍物之间的角度以及视差值之差。最后，我们使用深度图和灰度图像来验证车辆以改善性能。我们通过在各种实际交通情况下进行实验来验证我们提出的方法的性能。车辆检测的平均召回率为95.5％。

著录项

  • 来源
    《Optical engineering》 |2011年第2期|p.178-200|共23页
  • 作者

    Chung-Hee Lee; Young-Chul Lim; Soon Kwon; Jong-Hun Lee;

  • 作者单位

    Daegu Gyeongbuk Institute of Science and Technology Division of Advanced Industrial Science and Technology Room 511, 3rd Research Center DGIST, 223 Sang-ri Hyunpoong-Myun Dalseong-Gun Daegu 711 -873, Republic of Korea;

    Daegu Gyeongbuk Institute of Science and Technology Division of Advanced Industrial Science and Technology Room 511, 3rd Research Center DGIST, 223 Sang-ri Hyunpoong-Myun Dalseong-Gun Daegu 711 -873, Republic of Korea;

    Daegu Gyeongbuk Institute of Science and Technology Division of Advanced Industrial Science and Technology Room 511, 3rd Research Center DGIST, 223 Sang-ri Hyunpoong-Myun Dalseong-Gun Daegu 711 -873, Republic of Korea;

    Daegu Gyeongbuk Institute of Science and Technology Division of Advanced Industrial Science and Technology Room 511, 3rd Research Center DGIST, 223 Sang-ri Hyunpoong-Myun Dalseong-Gun Daegu 711 -873, Republic of Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    vehicle detection; stereo vision; road feature extraction; disparity histogram.;

    机译：车辆检测;立体视觉道路特征提取;视差直方图。;

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