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Multibody motion segmentation using the geometry of 6 points in 2D images.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2010 (English)In: International Conference on Pattern Recognition: ISSN 1051-4651, Institute of Electrical and Electronics Engineers (IEEE), 2010, 1783-1787 p.Conference paper, Published paper (Refereed)
Abstract [en]

We propose a method for segmenting an arbitrary number of moving objects using the geometry of 6 points in 2D images to infer motion consistency. This geometry allows us to determine whether or not observations of 6 points over several frames are consistent with a rigid 3D motion. The matching between observations of the 6 points and an estimated model of their configuration in 3D space, is quantified in terms of a geometric error derived from distances between the points and 6 corresponding lines in the image. This leads to a simple motion inconsistency score, based on the geometric errors of 6points that in the ideal case should be zero when the motion of the points can be explained by a rigid 3D motion. Initial point clusters are determined in the spatial domain and merged in motion trajectory domain based on this score. Each point is then assigned to the cluster, which gives the lowest score.Our algorithm has been tested with real image sequences from the Hopkins155 database with very good results, competing withthe state of the art methods, particularly for degenerate motion sequences. In contrast to the motion segmentation methods basedon multi-body factorization, that assume an affine camera model, the proposed method allows the mapping from 3D space to the 2D image to be fully projective.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2010. 1783-1787 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-63166DOI: 10.1109/ICPR.2010.440ISBN: 978-1-4244-7542-1 (print)OAI: oai:DiVA.org:liu-63166DiVA: diva2:376712
Conference
20th International Conference on Pattern Recognition (ICPR), 23-26 Aug. 2010
Available from: 2010-12-20 Created: 2010-12-13 Last updated: 2016-06-09Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • vancouver
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
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  • Other locale
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Output format
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