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KITTI-road
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33c75918-8ce2-11eb-b816-506b4b419b4c
1092ca4·
Jun 28, 2021 12:09 AM
·1Commits

Overview

The road and lane estimation benchmark consists of 289 training and 290 test images. We evaluate road and lane estimation performance in the bird's-eye-view space. It contains different categories of road scenes:

  • uu - urban unmarked (98/100)
  • um - urban marked (95/96)
  • umm - urban multiple marked lanes (96/94)
  • urban - combination of the three above

Data Collection

We equipped a standard station wagon with two high-resolution color and grayscale video cameras. Accurate ground truth is provided by a Velodyne laser scanner and a GPS localization system. Our datsets are captured by driving around the mid-size city of Karlsruhe, in rural areas and on highways. Up to 15 cars and 30 pedestrians are visible per image.

img

Citation

Please use the following citation when referencing the dataset:

@INPROCEEDINGS{Fritsch2013ITSC,
  author = {Jannik Fritsch and Tobias Kuehnl and Andreas Geiger},
  title = {A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms},
  booktitle = {International Conference on Intelligent Transportation Systems (ITSC)},
  year = {2013}
}
🎉感谢Hello Dataset的贡献
数据集信息
应用场景暂无
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LicenseCC BY-NC-SA 3.0
更新时间2020-12-31 17:27:49
数据概要
数据格式暂无
数据数量579
已标注数量0
文件大小2MB
版权归属方
Max Planck Institute for Intellgent Systems
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