dc.identifier.uri |
http://dx.doi.org/10.15488/16680 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/16807 |
|
dc.contributor.author |
El Amrani Abouelassad, S.
|
|
dc.contributor.author |
Mehltretter, M.
|
|
dc.contributor.author |
Rottensteiner, F.
|
|
dc.contributor.editor |
El-Sheimy, N.
|
|
dc.contributor.editor |
Abdelbary, A.A.
|
|
dc.contributor.editor |
El-Bendary, N.
|
|
dc.contributor.editor |
Mohasseb, Y.
|
|
dc.date.accessioned |
2024-03-20T10:11:26Z |
|
dc.date.available |
2024-03-20T10:11:26Z |
|
dc.date.issued |
2023 |
|
dc.identifier.citation |
El Amrani Abouelassad, S.; Mehltretter, M.; Rottensteiner, F.: Vehicle Pose and Shape Estimation in UAV Imagery Using a CNN. In: El-Sheimy, N.; Abdelbary, A.A.; El-Bendary, N.; Mohasseb, Y. (Eds.): ISPRS Geospatial Week 2023. Katlenburg-Lindau : Copernicus Publications, 2023 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; X-1/W1-2023), S. 935-944. DOI: https://doi.org/10.5194/isprs-annals-x-1-w1-2023-935-2023 |
|
dc.description.abstract |
Vehicle reconstruction from single aerial images is an important but challenging task. In this work, we introduce a new framework based on convolutional neural networks (CNN) that performs monocular detection, pose, shape and type estimation for vehicles in UAV imagery, taking advantage of a strong 3D object model. In the final training phase, all components of the model are trained end-to-end. We present a UAV-based dataset for the evaluation of our model and additionally evaluate it on an augmented version of the Hessingheim benchmark dataset. Our method presents encouraging pose and shape estimation results: Based on images of 3 cm GSD, it achieves median errors of up to 5 cm in position and 3◦ in orientation, and RMS errors of ±7 cm and ±24 cm in planimetry and height, respectively, for keypoints describing the car shape. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Katlenburg-Lindau : Copernicus Publications |
|
dc.relation.ispartof |
ISPRS Geospatial Week 2023 |
|
dc.relation.ispartofseries |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; X-1/W1-2023 |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0 |
|
dc.subject |
autonomous driving |
eng |
dc.subject |
Object detection |
eng |
dc.subject |
object reconstruction |
eng |
dc.subject |
pose estimation |
eng |
dc.subject |
shape estimation |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
550 | Geowissenschaften
|
|
dc.title |
Vehicle Pose and Shape Estimation in UAV Imagery Using a CNN |
eng |
dc.type |
BookPart |
|
dc.type |
Text |
|
dc.relation.essn |
2194-9050 |
|
dc.relation.doi |
https://doi.org/10.5194/isprs-annals-x-1-w1-2023-935-2023 |
|
dc.bibliographicCitation.volume |
X-1/W1-2023 |
|
dc.bibliographicCitation.firstPage |
935 |
|
dc.bibliographicCitation.lastPage |
944 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|