Scale and orientation adaptive mean shift tracking

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

119 Citations (Scopus)

Abstract

A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this study to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, whereas the scale and orientation changes cannot be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, the authors show that the original mean shift tracking algorithm can be derived using the zeroth- and the first-order moments of the weight image. With the zeroth-order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilises the estimated area and the second-order centre moment, is proposed to adaptively estimate the width, height and orientation changes of the target. Extensive experiments are performed to testify the proposed method and validate its robustness to the scale and orientation changes of the target.
Original languageEnglish
Pages (from-to)52-61
Number of pages10
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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