Publication detail

Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

AHMAD, T. CAMPR, P. ČADÍK, M. BEBIS, G.

Original Title

Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

Type

conference paper

Language

English

Original Abstract

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, second focused on visual geo-localization but relying on accurate detection of skyline and other two proposed for general semantic segmentation -- Fully Convolutional Networks (FCN) and SegNet. Each of the first two methods is trained on a common training set 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.

Keywords

horizon detection, skyline detection, geo-localization, segmentation methods, semantic segmentation, Fully Convolutional Networks

Authors

AHMAD, T.; CAMPR, P.; ČADÍK, M.; BEBIS, G.

Released

20. 2. 2017

Publisher

Institute of Electrical and Electronics Engineers

Location

Anchorage

ISBN

978-1-4799-1961-1

Book

Proceedings of the International Joint Conference on Neural Networks (IJCNN)

Pages from

4436

Pages to

4443

Pages count

8

URL

BibTex

@inproceedings{BUT133513,
  author="Touqeer {Ahmad} and Pavel {Campr} and Martin {Čadík} and George {Bebis}",
  title="Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection",
  booktitle="Proceedings of the International Joint Conference on Neural Networks (IJCNN)",
  year="2017",
  pages="4436--4443",
  publisher="Institute of Electrical and Electronics Engineers",
  address="Anchorage",
  doi="10.1109/IJCNN.2017.7966418",
  isbn="978-1-4799-1961-1",
  url="http://cadik.posvete.cz/papers/ahmad17comparison.pdf"
}