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Erschienen in:
Fine-Tuning for One-Look Regression Vehicle Counting in Low-Shot Aerial Datasets Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Modeling and Simulation Framework for Airborne Camera Systems

verfasst von : Marc-Antoine Drouin, Jonathan Fournier, Jonathan Boisvert, Louis Borgeat

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

Full Motion Video (FMV) and Wide Area Motion Imagery (WAMI) systems have been used extensively in the recent years to collect overhead imagery to produce value-added geospatial products. In order to better understand the limitations of these systems and to define the best flight conditions under which they can be operated, an integrated modeling and simulation (M&S) framework named Sensair was developed. This paper presents how this tool can be leveraged to simulate data collections with airborne camera systems using different parameters under varied conditions. Sensair can simulate realistic large-scale environments with moving vehicles and generate representative FMV and WAMI high-resolution imagery. The built-in interactive analysis tools enable the study of the different factors impacting the application of computer vision algorithms to aerial images. This paper presents Sensair’s M&S capabilities and describes use cases where the framework is used to perform tasks that include 3D reconstruction and the generation of an image dataset to support the evaluation of vehicle detection approaches.

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Vorheriges Kapitel An Efficient and Reasonably Simple Solution to the Perspective-Three-Point Problem
Nächstes Kapitel On the Development of a Classification Based Automated Motion Imagery Interpretability Prediction
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Metadaten
Titel
Modeling and Simulation Framework for Airborne Camera Systems
verfasst von
Marc-Antoine Drouin
Jonathan Fournier
Jonathan Boisvert
Louis Borgeat
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68792-2

Electronic ISBN: 978-3-030-68793-9

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68793-9_5