Homography-based Egomotion Estimation Using Gravity and SIFT Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00345989" target="_blank" >RIV/68407700:21230/21:00345989 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-69525-5_17" target="_blank" >https://doi.org/10.1007/978-3-030-69525-5_17</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-69525-5_17" target="_blank" >10.1007/978-3-030-69525-5_17</a>
Alternative languages
Result language
angličtina
Original language name
Homography-based Egomotion Estimation Using Gravity and SIFT Features
Original language description
Camera systems used, eg, in cars, UAVs, smartphones, and tablets, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector. Using the information from an IMU, the y-axes of cameras can be aligned with the gravity, reducing their relative orientation to a single DOF (degree of freedom). In this paper, we use the gravity information to derive extremely efficient minimal solvers for homography-based egomotion estimation from orientation-and scale-covariant features. We use the fact that orientation-and scale-covariant features, such as SIFT or ORB, provide additional constraints on the homography. Based on the prior knowledge about the target plane (horizontal/vertical/general plane, wrt the gravity direction) and using the SIFT/ORB constraints, we derive new minimal solvers that require fewer correspondences than traditional approaches and, thus, speed up the robust estimation procedure significantly. The proposed solvers are compared with the state-of-the-art point-based solvers on both synthetic data and real images, showing comparable accuracy and significant improvement in terms of speed. The implementation of our solvers is available at https://github. com/yaqding/relativepose-sift-gravity.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ACCV 2020: Proceedings of the 14th Asian Conference on Computer Vision, Part I
ISBN
978-3-030-69524-8
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
17
Pages from-to
278-294
Publisher name
Springer
Place of publication
Cham
Event location
Kyoto
Event date
Nov 30, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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