Title: OPEDD: Off-Road Pedestrian Detection Dataset
Authors: Neigel, Peter
Ameli, Mina
Katrolia, Jigyasa
Feld, Hartmut
Wasenmüller, Oliver
Stricker, Didier
Citation: Journal of WSCG. 2020, vol. 28, no. 1-2, p. 197-202.
Issue Date: 2020
Publisher: Václav Skala - UNION Agency
Document type: článek
URI: http://wscg.zcu.cz/WSCG2020/2020-J_WSCG-1-2.pdf
ISSN: 1213-6972 (print)
1213-6980 (CD-ROM)
1213-6964 (on-line)
Keywords: detekce chodců;segmentace instance;mimoměstské prostředí;terén;ADAS;užitková vozidla
Keywords in different language: pedestrian detection;instance segmentation;non-urban environment;off-road;ADAS;commercial vehicles
Abstract in different language: 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 excavators 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.
Rights: © Václav Skala - UNION Agency
Appears in Collections:Volume 28, Number 1-2 (2020)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/38442

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