Publication
OFFSED: Off-Road Semantic Segmentation Dataset
Peter Neigel; Jason Raphael Rambach; Didier Stricker
In: VISAPP 2021 Proceedings. International Conference on Computer Vision Theory and Applications (VISAPP-2021), 16th, February 8-10, Vienna, Austria, Pages 552-557, ISBN 978-989-758-488-6, SCITEPRESS Digital Library, 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.