Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Neigel, Peter | |
dc.contributor.author | Ameli, Mina | |
dc.contributor.author | Katrolia, Jigyasa | |
dc.contributor.author | Feld, Hartmut | |
dc.contributor.author | Wasenmüller, Oliver | |
dc.contributor.author | Stricker, Didier | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2020-07-24T08:38:26Z | |
dc.date.available | 2020-07-24T08:38:26Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Journal of WSCG. 2020, vol. 28, no. 1-2, p. 197-202. | en |
dc.identifier.issn | 1213-6972 (print) | |
dc.identifier.issn | 1213-6980 (CD-ROM) | |
dc.identifier.issn | 1213-6964 (on-line) | |
dc.identifier.uri | http://wscg.zcu.cz/WSCG2020/2020-J_WSCG-1-2.pdf | |
dc.identifier.uri | http://hdl.handle.net/11025/38442 | |
dc.format | 6 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | cs |
dc.relation.ispartofseries | Journal of WSCG | en |
dc.rights | © Václav Skala - UNION Agency | cs |
dc.subject | detekce chodců | cs |
dc.subject | segmentace instance | cs |
dc.subject | mimoměstské prostředí | cs |
dc.subject | terén | cs |
dc.subject | ADAS | cs |
dc.subject | užitková vozidla | cs |
dc.title | OPEDD: Off-Road Pedestrian Detection Dataset | en |
dc.type | článek | cs |
dc.type | article | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | 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. | en |
dc.subject.translated | pedestrian detection | en |
dc.subject.translated | instance segmentation | en |
dc.subject.translated | non-urban environment | en |
dc.subject.translated | off-road | en |
dc.subject.translated | ADAS | en |
dc.subject.translated | commercial vehicles | en |
dc.identifier.doi | https://doi.org/10.24132/JWSCG.2020.28.24 | |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | Volume 28, Number 1-2 (2020) |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
Neigel.pdf | Plný text | 2,43 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/38442
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