WildDash Benchmark

We are excited to be part of the Robust Vision Challenge 2018. Check out the challenge website and our submission instructions for further details on how to participate. We are looking forward to seeing you at CVPR!


For any questions or suggestions, please contact us: dashcamdataset@gmail.com.


We are currently finalizing the paper which describes our approach and should be referenced in your work when using our data. A pre-publish version will be uploaded to arXiv at the beginning of March.

The general principles of this work are introduced and described in our CV-HAZOP paper:

[1] How Good Is My Test Data? Introducing Safety Analysis for Computer Vision.
Zendel O., Murschitz M., Humenberger M., & Herzner W.
In International Journal of Computer Vision (IJCV), 2017.


Oliver Zendel
oliver.zendel (at) ait.ac.at

Markus Murschitz
markus.murschitz (at) ait.ac.at

Katrin Honauer
katrin.honauer (at) iwr.uni-heidelberg.de

Gustavo Fernandez
gustavojavier.fernandez (at) ait.ac.at

Daniel Steininger
daniel.steininger (at) ait.ac.at


  • 03.04.2018 The instance and semantic segmentation benchmarks are open for submissions.
  • 01.02.2018 Initial datasets for the segmentation benchmarks are available for download.


This project received financial support from the Horizon 2020 program of the European Union under the grant of the AutoDrive project "Advancing fail-aware, fail-safe, and fail-operational electronic components, systems, and architectures for fully automated driving to make future mobility safer, affordable, and end-user acceptable" (Grant No. 737469). Please visit www.autodrive-project.eu for more information.

We are grateful for the numerous authors who generously agreed to share frames of their dashcam videos for this research project. Individual attributions for the dashcam frames can be found in the download packages.

Furthermore, we gratefully acknowledge support for this research by the AIT Austrian Institute of Technology in Vienna and the Heidelberg Collaboratory for Image Processing (HCI).