Sequential Bayesian Model Update under Structured Scene Prior for Semantic Road Scenes Labeling

• Published in: 2013 IEEE International Conference on Computer Vision pp. 1321 - 1328
• Main Authors: Levinkov, Evgeny, Fritz, Mario
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2013
• Subjects: Adaptation models; Bayes methods; Bayesian analysis; Computer vision; Conferences; Data models; Labeling; Marking; Mathematical models; Roads; Run time (computers); Semantics; Training
• ISSN: 1550-5499; 2380-7504
• DOI: 10.1109/ICCV.2013.167

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. Existing adaptive methods for image labeling either require labeled data from the new condition or even operate globally on a complete test set. None of this is a desirable mode of operation for a system as described above where new images arrive sequentially and conditions may vary. 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.