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 Seiten 207-212 28 1-2 ISBN 1213-6972 7/2020.


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.

WSCG_OPEDD.pdf (pdf, 22 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence