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LISA Traffic Sign
2D Box Tracking
Autonomous Driving
|...
License: Unknown

Overview

The structure of the dataset is illustrated below:
img

Several source videos have been split up into tracks. The tracks are annotated with the sign
type they contain, but may include other signs. From each track, up to 30 frames are extracted
and all signs in these frames are tagged with position, type, and some additional meta data.

Contents

  • 47 US sign types
  • 7855 annotations on 6610 frames.
  • Sign sizes from 6x6 to 167x168 pixels.
  • Images obtained from different cameras. Image sizes vary from 640x480 to 1024x522 pixels.
  • Some images in color and some in grayscale.
  • Full version of the dataset includes videos for all annotated signs.
  • Each sign is annotated with sign type, position, size, occluded (yes/no), on side road (yes/no).
  • All annotations are save in plain text .csv-files.
  • Includes a set of Python tools to handle the annotations and easily extract relevant signs
    from the dataset.

Data Preview

Label Distribution

Citation

Please use the following citation when referencing the dataset:

@article{mogelmose2012vision,
  title={Vision-based traffic sign detection and analysis for intelligent driver assistance
systems: Perspectives and survey},
  author={Mogelmose, Andreas and Trivedi, Mohan Manubhai and Moeslund, Thomas B},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  volume={13},
  number={4},
  pages={1484--1497},
  year={2012},
  publisher={IEEE}
}
Data Summary
Type
Image,
Amount
6.61K
Size
7.75GB
Provided by
Computer Vision & Robotics Research Laboratory at University of California, San Diego
The Laboratory for Intelligent and Safe Automobiles (LISA) is a multidisciplinary effort to explore innovative approaches to making future automobiles safer and "intelligent".
| Amount 6.61K | Size 7.75GB
LISA Traffic Sign
2D Box Tracking
Autonomous Driving
License: Unknown

Overview

The structure of the dataset is illustrated below:
img

Several source videos have been split up into tracks. The tracks are annotated with the sign
type they contain, but may include other signs. From each track, up to 30 frames are extracted
and all signs in these frames are tagged with position, type, and some additional meta data.

Contents

  • 47 US sign types
  • 7855 annotations on 6610 frames.
  • Sign sizes from 6x6 to 167x168 pixels.
  • Images obtained from different cameras. Image sizes vary from 640x480 to 1024x522 pixels.
  • Some images in color and some in grayscale.
  • Full version of the dataset includes videos for all annotated signs.
  • Each sign is annotated with sign type, position, size, occluded (yes/no), on side road (yes/no).
  • All annotations are save in plain text .csv-files.
  • Includes a set of Python tools to handle the annotations and easily extract relevant signs
    from the dataset.

Data Preview

Label Distribution

Citation

Please use the following citation when referencing the dataset:

@article{mogelmose2012vision,
  title={Vision-based traffic sign detection and analysis for intelligent driver assistance
systems: Perspectives and survey},
  author={Mogelmose, Andreas and Trivedi, Mohan Manubhai and Moeslund, Thomas B},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  volume={13},
  number={4},
  pages={1484--1497},
  year={2012},
  publisher={IEEE}
}
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