Unsupervised object segmentation through change detection in a long term autonomy scenario
2016 (English)In: IEEE-RAS International Conference on Humanoid Robots, IEEE, 2016, 1181-1187 p.Conference paper (Refereed)
In this work we address the problem of dynamic object segmentation in office environments. We make no prior assumptions on what is dynamic and static, and our reasoning is based on change detection between sparse and non-uniform observations of the scene. We model the static part of the environment, and we focus on improving the accuracy and quality of the segmented dynamic objects over long periods of time. We address the issue of adapting the static structure over time and incorporating new elements, for which we train and use a classifier whose output gives an indication of the dynamic nature of the segmented elements. We show that the proposed algorithms improve the accuracy and the rate of detection of dynamic objects by comparing with a labelled dataset.
Place, publisher, year, edition, pages
IEEE, 2016. 1181-1187 p.
Anthropomorphic robots, Signal detection, Change detection, Dynamic nature, Dynamic objects, Non-uniform, Object segmentation, Office environments, Static structures, Object detection
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-202843DOI: 10.1109/HUMANOIDS.2016.7803420ScopusID: 2-s2.0-85010207172ISBN: 9781509047185 OAI: oai:DiVA.org:kth-202843DiVA: diva2:1082618
16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016, 15 November 2016 through 17 November 2016
QC 201703172017-03-172017-03-172017-03-17Bibliographically approved