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Bosch Small Traffic Lights
2D Box Tracking
Classification
Autonomous Driving
|...
License: Custom

Overview

We present the Bosch Small Traffic Lights Dataset, an accurate dataset for vision-based traffic
light detection. Vision-only based traffic light detection and tracking is a vital step on
the way to fully automated driving in urban environments. We hope that this dataset allows
for easy testing of objection detection approaches, especially for small objects in larger
images.
The scenes cover a decent variety of road scenes and typical difficulties:

Data description

This dataset contains 13427 camera images at a resolution of 1280x720 pixels and contains
about 24000 annotated traffic lights. The annotations include bounding boxes of traffic lights
as well as the current state (active light) of each traffic light.
The camera images are provided
as raw 12bit HDR images taken with a red-clear-clear-blue filter and as reconstructed 8-bit
RGB color images. The RGB images are provided for debugging and can also be used for training.
However, the RGB conversion process has some drawbacks. Some of the converted images may contain
artifacts and the color distribution may seem unusual.

Dataset specifications:

Training set:

  • 5093 images

  • Annotated about every 2 seconds

  • 10756 annotated traffic lights

  • Median traffic lights width: ~8.6 pixels

  • 15 different labels

  • 170 lights are partially occluded

Test set:

  • 8334 consecutive images

  • Annotated at about 15 fps

  • 13486 annotated traffic lights

  • Median traffic light width: 8.5 pixels

  • 4 labels (red, yellow, green, off)

  • 2088 lights are partially occluded

For the test set, every frame is annotated
and temporal information was used to improve the label accuracy. The test-set was recorded
independently from the training set, but within the same region. The data-set was created to
prototype traffic light detection approaches, it is not intended to cover all cases and not
to be used for production.

Data Preview

Label Distribution

Citation

Please use the following citation when referencing the dataset:

@inproceedings{BehrendtNovak2017ICRA,
  title={A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification},
  author={Behrendt, Karsten and Novak, Libor},
  booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
  organization={IEEE}
}

License

Custom

Data Summary
Type
Image,
Amount
13.427K
Size
25.24GB
Provided by
BOSCH
Bosch is a German multinational engineering and technology company headquartered in Gerlingen, near Stuttgart, Germany. The company was founded by Robert Bosch in Stuttgart in 1886.
| Amount 13.427K | Size 25.24GB
Bosch Small Traffic Lights
2D Box Tracking Classification
Autonomous Driving
License: Custom

Overview

We present the Bosch Small Traffic Lights Dataset, an accurate dataset for vision-based traffic
light detection. Vision-only based traffic light detection and tracking is a vital step on
the way to fully automated driving in urban environments. We hope that this dataset allows
for easy testing of objection detection approaches, especially for small objects in larger
images.
The scenes cover a decent variety of road scenes and typical difficulties:

Data description

This dataset contains 13427 camera images at a resolution of 1280x720 pixels and contains
about 24000 annotated traffic lights. The annotations include bounding boxes of traffic lights
as well as the current state (active light) of each traffic light.
The camera images are provided
as raw 12bit HDR images taken with a red-clear-clear-blue filter and as reconstructed 8-bit
RGB color images. The RGB images are provided for debugging and can also be used for training.
However, the RGB conversion process has some drawbacks. Some of the converted images may contain
artifacts and the color distribution may seem unusual.

Dataset specifications:

Training set:

  • 5093 images

  • Annotated about every 2 seconds

  • 10756 annotated traffic lights

  • Median traffic lights width: ~8.6 pixels

  • 15 different labels

  • 170 lights are partially occluded

Test set:

  • 8334 consecutive images

  • Annotated at about 15 fps

  • 13486 annotated traffic lights

  • Median traffic light width: 8.5 pixels

  • 4 labels (red, yellow, green, off)

  • 2088 lights are partially occluded

For the test set, every frame is annotated
and temporal information was used to improve the label accuracy. The test-set was recorded
independently from the training set, but within the same region. The data-set was created to
prototype traffic light detection approaches, it is not intended to cover all cases and not
to be used for production.

Data Preview

Label Distribution

Citation

Please use the following citation when referencing the dataset:

@inproceedings{BehrendtNovak2017ICRA,
  title={A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification},
  author={Behrendt, Karsten and Novak, Libor},
  booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
  organization={IEEE}
}

License

Custom

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