• Computer Science
  • Published in ArXiv 2018

BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling

@article{Yu2018BDD100KAD,
  title={BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling},
  author={Fisher Yu and Wenqi Xian and Yingying Chen and Fangchen Liu and Mike Liao and Vashisht Madhavan and Trevor Darrell},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.04687}
}
Datasets drive vision progress and autonomous driving is a critical vision application, yet existing driving datasets are impoverished in terms of visual content. Driving imagery is becoming plentiful, but annotation is slow and expensive, as annotation tools have not kept pace with the flood of data. Our first contribution is the design and implementation of a scalable annotation system that can provide a comprehensive set of image labels for large-scale driving datasets. Our second… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 26 REFERENCES

The Cityscapes Dataset for Semantic Urban Scene Understanding

VIEW 12 EXCERPTS
HIGHLY INFLUENTIAL

Microsoft COCO: Common Objects in Context

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

1 year, 1000 km: The Oxford RobotCar dataset

VIEW 1 EXCERPT

Annotating Object Instances with a Polygon-RNN

VIEW 1 EXCERPT

CityPersons: A Diverse Dataset for Pedestrian Detection

VIEW 1 EXCERPT

Dilated Residual Networks

VIEW 3 EXCERPTS

The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

VIEW 2 EXCERPTS

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

  • Seokju Lee, Junsik Kim, +7 authors In So Kweon
  • Computer Science
  • 2017 IEEE International Conference on Computer Vision (ICCV)
  • 2017
VIEW 2 EXCERPTS

End-to-End Learning of Driving Models from Large-Scale Video Datasets

VIEW 1 EXCERPT