Camera Elevation Estimation from a Single Mountain Landscape Photograph
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00236246" target="_blank" >RIV/68407700:21230/15:00236246 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26230/15:PU117026
Result on the web
<a href="http://dx.doi.org/10.5244/C.29.30" target="_blank" >http://dx.doi.org/10.5244/C.29.30</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5244/C.29.30" target="_blank" >10.5244/C.29.30</a>
Alternative languages
Result language
angličtina
Original language name
Camera Elevation Estimation from a Single Mountain Landscape Photograph
Original language description
This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment. We introduce a new benchmark dataset of one-hundred thousand images with annotated camera elevation called Alps100K. We propose and experimentally evaluate two automatic data-driven approaches to camera elevation estimation: one based on convolutional neural networks, the other on local features. To compare the proposed methods to human performance, an experiment with 100 subjects is conducted. The experimental results show that both proposed approaches outperform humans and that the best result is achieved by their combination.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/LL1303" target="_blank" >LL1303: Large Scale Category Retrieval</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the British Machine Vision Conference (BMVC)
ISBN
978-1-901725-53-7
ISSN
—
e-ISSN
—
Number of pages
12
Pages from-to
—
Publisher name
British Machine Vision Association
Place of publication
London
Event location
Swansea
Event date
Sep 7, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—