Scene Segmentation in Adverse Vision Conditions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

Semantic road labeling is a key component of systems that aim at assisted or even autonomous driving. Considering that such systems continuously operate in the real-world, unforeseen conditions not represented in any conceivable training procedure are likely to occur on a regular basis. In order to equip systems with the ability to cope with such situations, we would like to enable adaptation to such new situations and conditions at runtime. We study the effect of changing test conditions on scene labeling methods based on a new diverse street scene dataset. We propose a novel approach that can operate in such conditions and is based on a sequential Bayesian model update in order to robustly integrate the arriving images into the adapting procedure.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany

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