Memory-based state prediction in statistical visual object tracking

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

Abstract

Conventional model-based visual tracking assumes a mathematical state prediction model in advance. Thanks to the prediction model, a tracker can locate a target in visual clutters. However, if the target moves against the pre-defined prediction model, the tracker can easily miss the target. To overcome this problem, we introduce memory-based state prediction that a tracker can learn object's motion on-the-fly at the tracking. In addition, we propose a new framework in visual object tracking which integrates the memory-based state prediction into a conventional mathematical based state prediction. Our experiments suggest that our new framework permits a visual tracker to learn and track unexpected motion in the real world.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Computational Intelligence
Pages444-449
Number of pages6
Publication statusPublished - 2005 Dec 1
EventIASTED International Conference on Computational Intelligence - Calgary, AB, Canada
Duration: 2005 Jul 42005 Jul 6

Publication series

NameProceedings of the IASTED International Conference on Computational Intelligence
Volume2005

Other

OtherIASTED International Conference on Computational Intelligence
CountryCanada
CityCalgary, AB
Period05/7/405/7/6

Keywords

  • Computer vision
  • Memory-based state prediction
  • Particle filter

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Nakajima, T. (2005). Memory-based state prediction in statistical visual object tracking. In Proceedings of the IASTED International Conference on Computational Intelligence (pp. 444-449). (Proceedings of the IASTED International Conference on Computational Intelligence; Vol. 2005).