Sky segmentation in the wild: An empirical study

Radu P. Mihail, Scott Workman, Zach Bessinger, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Scopus citations

Abstract

Automatically determining which pixels in an image view the sky, the problem of sky segmentation, is a critical preprocessing step for a wide variety of outdoor image interpretation problems, including horizon estimation, robot navigation and image geolocalization. Many methods for this problem have been proposed with recent work achieving significant improvements on benchmark datasets. However, such datasets are often constructed to contain images captured in favorable conditions and, therefore, do not reflect the broad range of conditions with which a real-world vision system must cope. This paper presents the results of a large-scale empirical evaluation of the performance of three state-of-the-art approaches on a new dataset, which consists of roughly 100k images captured "in the wild". The results show that the performance of these methods can be dramatically degraded by the local lighting and weather conditions. We propose a deep learning based variant of an ensemble solution that outperforms the methods we tested, in some cases achieving above 50% relative reduction in misclassified pixels. While our results show there is room for improvement, our hope is that this dataset will encourage others to improve the real-world performance of their algorithms.

Original languageEnglish
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Publication series

Name2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision, WACV 2016
Country/TerritoryUnited States
CityLake Placid
Period3/7/163/10/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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