Publication
OPEDD: Off-Road Pedestrian Detection Dataset
Peter Neigel; Mina Ameli; Jigyasa Singh Katrolia; Hartmut Feld; Oliver Wasenmüller; Didier Stricker
In: Journal of WSCG. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG-2020), May 19-21, Virtual (due to CoVid-19), Czech Republic, Pages 207-212, Vol. 28, No. 1-2, ISBN 1213-6972, 7/2020.
Abstract
The detection of pedestrians plays an essential part in the development of automated driver assistance systems.
Many of the currently available datasets for pedestrian detection focus on urban environments. State-of-the-art
neural networks trained on these datasets struggle in generalizing their predictions from one environment to a
visually dissimilar one, limiting the use case to urban scenes. Commercial working machines like tractors or exca-
vators make up a substantial share of the total number of motorized vehicles and are often situated in fundamentally
different surroundings, e.g. forests, meadows, construction sites or farmland. In this paper, we present a dataset
for pedestrian detection which consists of 1018 stereo-images showing varying numbers of persons in differing
non-urban environments and comes with manually annotated pixel-level segmentation masks and bounding boxes.