OFFSED: Off-Road Semantic Segmentation Dataset

Peter Neigel, Peter Neigel, Jason Rambach, Didier Stricker, Didier Stricker

2021

Abstract

Over the last decade, improvements in neural networks have facilitated substantial advancements in automated driver assistance systems. In order to manage navigating its surroundings reliably and autonomously, self- driving vehicles need to be able to infer semantic information of the environment. Large parts of the research corpus focus on private passenger cars and cargo trucks, which share the common environment of paved roads, highways and cities. Industrial vehicles like tractors or excavators however make up a substantial share of the total number of motorized vehicles globally while operating in fundamentally different environments. In this paper, we present an extension to our previous Off-Road Pedestrian Detection Dataset (OPEDD) that extends the ground truth data of 203 images to full image semantic segmentation masks which assign one of 19 classes to every pixel. The selection of images was done in a way that captures the whole range of environments and human poses depicted in the original dataset. In addition to pixel labels, a few selected countable classes also come with instance identifiers. This allows for the use of the dataset in instance and panoptic segmentation tasks.

Paper Citation

in Harvard Style

Neigel P., Rambach J. and Stricker D. (2021). OFFSED: Off-Road Semantic Segmentation Dataset. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 552-557. DOI: 10.5220/0010349805520557

in Bibtex Style

@conference{visapp21,
author={Peter Neigel and Jason Rambach and Didier Stricker},
title={OFFSED: Off-Road Semantic Segmentation Dataset},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={552-557},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010349805520557},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - OFFSED: Off-Road Semantic Segmentation Dataset
SN - 978-989-758-488-6
AU - Neigel P.
AU - Rambach J.
AU - Stricker D.
PY - 2021
SP - 552
EP - 557
DO - 10.5220/0010349805520557
PB - SciTePress