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Combine Kalman filter and particle filter to improve color tracking algorithm

  • Sungkyunkwan University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In machine vision, color tracking is a well known problem. The Kalman filter or particle filter are often used to build color tracking algorithms. The Kalman filter is good in tracking a linear system, but it often misses the object when the object changes its direction suddenly. In this case, the particle filter is used but it fails easily when the object moves too fast. This paper presents another method to track a rigid object. Based on combining the Kalman filter and particle filter, this method increases the accuracy and speed of the color tracking algorithm.

Original languageEnglish
Title of host publicationICCAS 2007 - International Conference on Control, Automation and Systems
Pages558-561
Number of pages4
DOIs
StatePublished - 2007
EventInternational Conference on Control, Automation and Systems, ICCAS 2007 - Seoul, Korea, Republic of
Duration: 17 Oct 200720 Oct 2007

Publication series

NameICCAS 2007 - International Conference on Control, Automation and Systems

Conference

ConferenceInternational Conference on Control, Automation and Systems, ICCAS 2007
Country/TerritoryKorea, Republic of
CitySeoul
Period17/10/0720/10/07

Keywords

  • Color tracking
  • CPF
  • Kalman filter
  • Particle filter

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