Robust mean-shift tracking with corrected background-weighted histogram

J. Ning, Lei Zhang, Dapeng Zhang, C. Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

172 Citations (Scopus)

Abstract

The background-weighted histogram (BWH) algorithm proposed by Comaniciu et al. attempts to reduce the interference of background in target localisation in mean-shift tracking. However, the authors prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, that is, BWH does not introduce any new information because the mean-shift iteration formula is invariant to the scale transformation of weights. Then a corrected BWH (CBWH) formula is proposed by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background's interference in target localisation. The experimental results show that CBWH can lead to faster convergence and more accurate localisation than the usual target representation in mean-shift tracking. Even if the target is not well initialised, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventional target representation.
Original languageEnglish
Pages (from-to)62-69
Number of pages8
JournalIET Computer Vision
Volume6
Issue number1
DOIs
Publication statusPublished - 1 Jan 2012

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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