ML Anthology

Bounding Box Dataset Augmentation for Long-Range Object Distance Estimation

Marten Franke, Vaishnavi Gopinath, Chaitra Reddy, Danijela Ristic-Durrant, Kai Michels
ICCVW 2021 pp. 1669-1677
doi:10.1109/ICCVW54120.2021.00192 /iccvw/2021/franke2021iccvw-bounding/

Abstract

Autonomous long-range obstacle detection and distance estimation plays an important role in numerous applications such as railway applications when it comes to locomotive drivers support or developments towards driverless trains. To overcome the problem of small training datasets, this paper presents two data augmentation methods for training the ANN DisNet to perform reliable long-range distance estimation.

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Cite

Text

Franke et al. "Bounding Box Dataset Augmentation for Long-Range Object Distance Estimation." IEEE/CVF International Conference on Computer Vision Workshops, 2021. doi:10.1109/ICCVW54120.2021.00192

Markdown

[Franke et al. "Bounding Box Dataset Augmentation for Long-Range Object Distance Estimation." IEEE/CVF International Conference on Computer Vision Workshops, 2021.](https://mlanthology.org/iccvw/2021/franke2021iccvw-bounding/) doi:10.1109/ICCVW54120.2021.00192

BibTeX

@inproceedings{franke2021iccvw-bounding,
  title     = {{Bounding Box Dataset Augmentation for Long-Range Object Distance Estimation}},
  author    = {Franke, Marten and Gopinath, Vaishnavi and Reddy, Chaitra and Ristic-Durrant, Danijela and Michels, Kai},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2021},
  pages     = {1669-1677},
  doi       = {10.1109/ICCVW54120.2021.00192},
  url       = {https://mlanthology.org/iccvw/2021/franke2021iccvw-bounding/}
}