Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932070" target="_blank" >RIV/49777513:23520/17:43932070 - isvavai.cz</a>

Result on the web

<a href="http://ieeexplore.ieee.org/document/7966418/" target="_blank" >http://ieeexplore.ieee.org/document/7966418/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN.2017.7966418" target="_blank" >10.1109/IJCNN.2017.7966418</a>

Result language

angličtina

Original language name

Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

Original language description

Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on user-in-the-loop skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such methods for autonomous horizon/sky line detection on an extensive data set. Specifically, we provide the comparison between four recently proposed segmentation methods; one explicitly targeting the problem of horizon detection[2], second focused on visual geolocalization but relying on accurate detection of skyline [15] nd other two proposed for general semantic segmentation – Fully Convolutional Networks (FCN) [21] and SegNet[22]. Each of the first two methods is trained on a common training set [11] comprised of about 200 images while models for the third and fourth method are fine tuned for sky segmentation problem through transfer learning using the same data set. Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions. We report average accuracy and average absolute pixel error for each of the presented formulation.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20205 - Automation and control systems

Project

<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Neural Networks (IJCNN), 2017 International Joint Conference on

ISBN

978-1-5090-6182-2

ISSN

2161-4393

e-ISSN

—

Number of pages

8

Pages from-to

4436-4443

Publisher name

IEEE

Place of publication

New York

Event location

Anchorage, Alaska, USA

Event date

May 14, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000426968704091