DTLD
2D Box Tracking
Autonomous Driving
|...
许可协议: Unknown

Overview

DTLD contains more than 230,000 annotated traffic lights in camera images with a resolution of 2 megapixels. The dataset was recorded in 11 cities in Germany with a frequency of 15 Hz. Due to additional annotation attributes such as the traffic light pictogram, orientation or relevancy 344 unique classes exist. In addition to camera images and labels we provide stereo information in form of disparity images allowing stereo-based detection and depth-dependent evaluations.

Labeling down to the resolution limit

Our sophisticated labeling tool allows to annotate traffic lights down to a width of 5 pixels or below. Therefore DTLD consists of a high number of very small objects (Figure 2). This makes the dataset also predestined for researchers working on small object detection.

High aspect ratio variance

By also annotating traffic lights consisting of one, two (e.g. pedestrian traffic lights) or four light units (e.g. bus/tram traffic lights) DTLD has a higher aspect ratio variance than other datasets.

High regional variance

One important property of DTLD is its high regional variance (Figure 4), i.e. the distribution of the labels in the 2D image. This was reached by only adding non-static scenes to the dataset. Only static scenes, in which at least one traffic light state changes are added.

img

Dataset statistics

Labels

We provide annotations in terms of bounding box coordinates (top left corner, width and height) but also extensive traffic lights properties, namely:

  • Viewpoint orientation
  • Relevancy
  • Installation orientation
  • Number of light units
  • State
  • Pictogram

These properties are expressed by a six digit class identity. In addition each label has a track identity (useful for tracking evaluations). For each image, a timestamp and vehicle data (GPS, velocity and yaw rate) is available.

Class identity

Digit I: Viewpoint orientation

Viewpoint orientation describes the orientation of a traffic light with respect to the ego-vehicle. There exist 4 tags:

  • Front: valid for the ego-vehicle and the state/pictogram is mostly visible
  • Back: traffic lights are valid for the oncoming traffic
  • Left: traffic lights turned to the left, mostly pedestrian traffic lights
  • Right: Traffic lights turned to the right, mostly pedestrian traffic lights

Digit II: Relevancy/Occlusion

  • Relevancy: A traffic light is relevant if it is valid for the planned route of the vehicle. Traffic lights of the next intersection are not tagged as relevant.
  • Occlusion: Concludes occluded and truncated traffic lights

Digit III: Installation orientation

  • horizontal: Horizontally orientated traffic lights, mostly in Asia or US
  • vertical: Vertically orientated traffic lights

Digit IV: Number of light units

Self-explanatory

Digit V: State

Self-explanatory. Red-Yellow is a transition state in Germany indicating the change from red to green.

Digit VI: Pictogram

Expressing the mask of the lamp (circular, arrow + direction, pedestrian, bike ...)

img Six digit class identity

Track identity

Each unique traffic light instance has an assigned track identity during one sequence. One sequence consists of one drive to an intersection until passing it.

Vehicle data

DTLD provides temporal information in the form of one timestamp per image as well as local information in the form of GPS information.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{fregin2018driveu,
  title={The DriveU traffic light dataset: Introduction and comparison with existing datasets},
  author={Fregin, Andreas and M{\"u}ller, Julian and Kre$\beta$el, Ulrich and Dietmayer, Klaus},
  booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={3376--3383},
  year={2018},
  organization={IEEE}
}
数据概要
数据格式
Image,
数据量
230K
文件大小
--
发布方
Ulm University
Ulm University is a public university in Ulm, Baden-Württemberg, Germany. The university was founded in 1967 and focuses on natural sciences, medicine, engineering sciences, mathematics, economics and computer science.
数据集反馈
| 13 | 数据量 230K | 大小 --
DTLD
2D Box Tracking
Autonomous Driving
许可协议: Unknown

Overview

DTLD contains more than 230,000 annotated traffic lights in camera images with a resolution of 2 megapixels. The dataset was recorded in 11 cities in Germany with a frequency of 15 Hz. Due to additional annotation attributes such as the traffic light pictogram, orientation or relevancy 344 unique classes exist. In addition to camera images and labels we provide stereo information in form of disparity images allowing stereo-based detection and depth-dependent evaluations.

Labeling down to the resolution limit

Our sophisticated labeling tool allows to annotate traffic lights down to a width of 5 pixels or below. Therefore DTLD consists of a high number of very small objects (Figure 2). This makes the dataset also predestined for researchers working on small object detection.

High aspect ratio variance

By also annotating traffic lights consisting of one, two (e.g. pedestrian traffic lights) or four light units (e.g. bus/tram traffic lights) DTLD has a higher aspect ratio variance than other datasets.

High regional variance

One important property of DTLD is its high regional variance (Figure 4), i.e. the distribution of the labels in the 2D image. This was reached by only adding non-static scenes to the dataset. Only static scenes, in which at least one traffic light state changes are added.

img

Dataset statistics

Labels

We provide annotations in terms of bounding box coordinates (top left corner, width and height) but also extensive traffic lights properties, namely:

  • Viewpoint orientation
  • Relevancy
  • Installation orientation
  • Number of light units
  • State
  • Pictogram

These properties are expressed by a six digit class identity. In addition each label has a track identity (useful for tracking evaluations). For each image, a timestamp and vehicle data (GPS, velocity and yaw rate) is available.

Class identity

Digit I: Viewpoint orientation

Viewpoint orientation describes the orientation of a traffic light with respect to the ego-vehicle. There exist 4 tags:

  • Front: valid for the ego-vehicle and the state/pictogram is mostly visible
  • Back: traffic lights are valid for the oncoming traffic
  • Left: traffic lights turned to the left, mostly pedestrian traffic lights
  • Right: Traffic lights turned to the right, mostly pedestrian traffic lights

Digit II: Relevancy/Occlusion

  • Relevancy: A traffic light is relevant if it is valid for the planned route of the vehicle. Traffic lights of the next intersection are not tagged as relevant.
  • Occlusion: Concludes occluded and truncated traffic lights

Digit III: Installation orientation

  • horizontal: Horizontally orientated traffic lights, mostly in Asia or US
  • vertical: Vertically orientated traffic lights

Digit IV: Number of light units

Self-explanatory

Digit V: State

Self-explanatory. Red-Yellow is a transition state in Germany indicating the change from red to green.

Digit VI: Pictogram

Expressing the mask of the lamp (circular, arrow + direction, pedestrian, bike ...)

img Six digit class identity

Track identity

Each unique traffic light instance has an assigned track identity during one sequence. One sequence consists of one drive to an intersection until passing it.

Vehicle data

DTLD provides temporal information in the form of one timestamp per image as well as local information in the form of GPS information.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{fregin2018driveu,
  title={The DriveU traffic light dataset: Introduction and comparison with existing datasets},
  author={Fregin, Andreas and M{\"u}ller, Julian and Kre$\beta$el, Ulrich and Dietmayer, Klaus},
  booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={3376--3383},
  year={2018},
  organization={IEEE}
}
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