Stereo vision based motion parameter estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The motion parameter estimation for a class of movements in the space by using stereo vision is considered by observing a group of points. The considered motion equation can cover a wide class of practical movements in the space. The observability of this class of movement is clarified. The estimation algorithm for the motion parameters which are all time-varying is developed based on the second method of Lyapunov. The assumptions about the perspective system are reasonable and have apparently physical interpretations. The proposed recursive algorithm requires minor a priori knowledge about the system. Experimental results show the proposed algorithm is effective even in the presence of measurement noises.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages371-380
Number of pages10
Volume5755 LNAI
DOIs
Publication statusPublished - 2009
Event5th International Conference on Intelligent Computing, ICIC 2009 - Ulsan
Duration: 2009 Sep 162009 Sep 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5755 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th International Conference on Intelligent Computing, ICIC 2009
CityUlsan
Period09/9/1609/9/19

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Keywords

  • Estimation
  • Motion parameter
  • Stereo vision

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Chen, X. (2009). Stereo vision based motion parameter estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5755 LNAI, pp. 371-380). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5755 LNAI). https://doi.org/10.1007/978-3-642-04020-7_39