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 article |
URI: | http://wscg.zcu.cz/WSCG2020/2020-J_WSCG-1-2.pdf http://hdl.handle.net/11025/38442 |
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) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Neigel.pdf | Plný text | 2,43 MB | Adobe PDF | View/Open |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/38442
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.