OPEDD: Off-Road Pedestrian Detection Dataset

OPEDD: Off-Road Pedestrian Detection Dataset
Peter Neigel, Mina Ameli, Jigyasa Singh Katrolia, Hartmut Feld, Oliver Wasenmüller, Didier Stricker
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 197-202 28 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.

The Dataset is available for download here: http://www.dfki.uni-kl.de/~neigel/offsed.html