Abstract
Obtaining 3D ground plane equations from remote sensing device data is crucial in scene-understanding tasks (e.g. camera parameters, distance of an object to the ground plane). Equations describing the orientation of the ground plane of a scene in 2D or 3D space can be reconstructed from multiple sensor output data as collected from 2D or 3D sensors such as; RGB-D cameras, T-o-F cameras or LiDAR sensors. In our work, we propose a modular and simple pipeline for 3D ground plane detection from 2D-RGB images for subsequent distance estimation of a given object to the ground plane. As the proposed algorithm can be applied on 2D-RGB images, provided by common devices such as surveillance cameras, we provide evidence that the algorithm has the potential to advance automated surveillance systems such as devices used for fall detection without the need to change existing hardware.
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Acknowledgements
The research reported in this paper has been funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Digital and Economic Affairs (BMDW), and the State of Upper Austria in the frame of SCCH, a center in the COMET - Competence Centers for Excellent Technologies Programme managed by Austrian Research Promotion Agency FFG.
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Cakiroglu, O., Wieser, V., Zellinger, W., Souza Ribeiro, A., Kloihofer, W., Kromp, F. (2022). Detection of the 3D Ground Plane from 2D Images for Distance Measurement to the Ground. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_5
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