Object tracking by multi-degrees of freedom mean shift procedure combined with the Kalman particle filter algorithm

Jing Ping Jia, Qing Wang, Yan Mei Chai, Rong Chun Zhao

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

7 Scopus citations

Abstract

The paper begins with an analysis of the shortcomings of existing methods. We aim to overcome these shortcomings with our improved mean shift algorithm in which we introduce two distinguishing features: the bandwidth matrix and the target angle. We first introduce the bandwidth matrix mean shift procedure. Then we describe the target by introducing the target rectangle, which provides two positions coordinates of the centre point, the horizontal axis, the vertical axis and the target angle, altogether five degrees of freedom. Target angle is used to accommodate the rotation of objects while the two axes determine the size in two independent directions. Furthermore, we incorporate the Kalman Particle Filter (KPF) into our tracking framework to cope with a temporal occlusion of the objects. Experiments with several real worlds' sequences indicate our new method's capability to adapt to any combinations of the target's rotation, zooming and translation. With better description of the object it achieves much better precision.

Original languageEnglish
Title of host publicationProceedings of the 2006 International Conference on Machine Learning and Cybernetics
Pages3793-3797
Number of pages5
DOIs
StatePublished - 2006
Event2006 International Conference on Machine Learning and Cybernetics - Dalian, China
Duration: 13 Aug 200616 Aug 2006

Publication series

NameProceedings of the 2006 International Conference on Machine Learning and Cybernetics
Volume2006

Conference

Conference2006 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityDalian
Period13/08/0616/08/06

Keywords

  • Adaptability
  • Bandwidth matrix
  • Kalman particle filter
  • Mean shift
  • Target angle
  • Tracking of objects in image sequences

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