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The Boxy Vehicles
2D Box
2D Cuboid
Autonomous Driving
|...
License: Custom

Overview

A large vehicle detection dataset with almost two million annotated vehicles for training and
evaluating object detection methods for self-driving cars on freeways.

QUICK SPECS:

  • 200,000 images
  • 1,990,000 annotated vehicles
  • 5 Megapixel resolution
  • Sunshine, rain, dusk, night
  • Clear freeways, heavy traffic, traffic jams

2D BOXES OR 3D CUBOIDS FOR VEHICLES

Axis-aligned bounding boxes (AABB) and trapezoids for a more accurate description.
Simple AABB may intrude into neighboring lanes, as shown below.

Annotation with truck carrying a car

Data Annotation

2D DETECTIONS

Apply one of the best studied tasks in computer vision, 2D axes aligned bounding box detections
in camera images, to vehicles for automated driving. See how far it is possible to take 2D
detections on almost 2 million annotated vehicles. The benchmark is scored based on average
precision. All kinds of approaches, external approaches, closed source, and any runtime are
allowed. We may add benchmarks or metrics and welcome suggestions.

img

3D / POLYGON DETECTIONS

Axes aligned 2D bounding
boxes unfortunately often also contain neighboring lanes which is inconvient for behavior planning
and sensor fusion. The 2D boxes also do not convey any orientation and size information. Splitting
the annotation into the two visible sides of a vehicle offers far higher accuracy and additional
information. This benchmark is scored on the average precision of the overall polygon, the
sides, and rears of the vehicles.

img

REALTIME DETECTIONS

Automated vehicles have to be able to react to objects in real-time
and while they can track and predict vehicles over time, fast detections are essential. For
this benchmark, we restrict the compute time to 50 ms per incoming image which allows to process
20 images a second. There are no restrictions on image input size, hardware, or software but
they should be reported. Additional splits, e.g. based on computer power, are possible.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{behrendt2019boxy,
  title={Boxy Vehicle Detection in Large Images},
  author={Behrendt, Karsten},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
  pages={0--0},
  year={2019}
}

License

Custom

Data Summary
Type
Image,
Amount
200K
Size
851.62GB
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 200K | Size 851.62GB
The Boxy Vehicles
2D Box 2D Cuboid
Autonomous Driving
License: Custom

Overview

A large vehicle detection dataset with almost two million annotated vehicles for training and
evaluating object detection methods for self-driving cars on freeways.

QUICK SPECS:

  • 200,000 images
  • 1,990,000 annotated vehicles
  • 5 Megapixel resolution
  • Sunshine, rain, dusk, night
  • Clear freeways, heavy traffic, traffic jams

2D BOXES OR 3D CUBOIDS FOR VEHICLES

Axis-aligned bounding boxes (AABB) and trapezoids for a more accurate description.
Simple AABB may intrude into neighboring lanes, as shown below.

Annotation with truck carrying a car

Data Annotation

2D DETECTIONS

Apply one of the best studied tasks in computer vision, 2D axes aligned bounding box detections
in camera images, to vehicles for automated driving. See how far it is possible to take 2D
detections on almost 2 million annotated vehicles. The benchmark is scored based on average
precision. All kinds of approaches, external approaches, closed source, and any runtime are
allowed. We may add benchmarks or metrics and welcome suggestions.

img

3D / POLYGON DETECTIONS

Axes aligned 2D bounding
boxes unfortunately often also contain neighboring lanes which is inconvient for behavior planning
and sensor fusion. The 2D boxes also do not convey any orientation and size information. Splitting
the annotation into the two visible sides of a vehicle offers far higher accuracy and additional
information. This benchmark is scored on the average precision of the overall polygon, the
sides, and rears of the vehicles.

img

REALTIME DETECTIONS

Automated vehicles have to be able to react to objects in real-time
and while they can track and predict vehicles over time, fast detections are essential. For
this benchmark, we restrict the compute time to 50 ms per incoming image which allows to process
20 images a second. There are no restrictions on image input size, hardware, or software but
they should be reported. Additional splits, e.g. based on computer power, are possible.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{behrendt2019boxy,
  title={Boxy Vehicle Detection in Large Images},
  author={Behrendt, Karsten},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
  pages={0--0},
  year={2019}
}

License

Custom

0
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