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
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. | |
License of this version: | CC BY 4.0 Unported |
Document Type: | BookPart |
Publishing status: | publishedVersion |
Issue Date: | 2023 |
Appears in Collections: | Fakultät für Bauingenieurwesen und Geodäsie |
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