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Unsupervised object segmentation through change detection in a long term autonomy scenario
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-7796-1438
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-1170-7162
2016 (English)In: IEEE-RAS International Conference on Humanoid Robots, IEEE, 2016, 1181-1187 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.
Keyword [en]
Anthropomorphic robots, Signal detection, Change detection, Dynamic nature, Dynamic objects, Non-uniform, Object segmentation, Office environments, Static structures, Object detection
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-202843DOI: 10.1109/HUMANOIDS.2016.7803420ISI: 000403009300175Scopus ID: 2-s2.0-85010207172ISBN: 9781509047185 (print)OAI: oai:DiVA.org:kth-202843DiVA: diva2:1082618
Conference
16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016, 15 November 2016 through 17 November 2016
Note

QC 20170317

Available from: 2017-03-17 Created: 2017-03-17 Last updated: 2017-07-03Bibliographically approved

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Ambrus, RaresFolkesson, JohnJensfelt, Patric

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