Single object tracking via robust combination of particle filter and sparse representation

Shuangyan Yi, Zhenyu He*, Xinge You, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

Abstract

The drifting problem is a core problem in single object tracking and attracts many researchers' attention. Unfortunately, traditional methods cannot well solve the drifting problem. In this paper, we propose a tracking method based on the robust combination of particle filter and reverse sparse representation (RC-PFRSR) to reduce the drifting. First, we find the ill-organized coefficients. Second, we propose a diagonal matrix α, whose diagonal line includes each patch contribution factor, to function each patch coefficient value of one candidate obtained by sparse representation. Third, we adaptively discriminate the power of each patch within the current candidate region by an occlusion prediction scheme. Our experimental results on nine challenging video sequences show that our RC-PFRSR method is effective and outperforms six state-of-the-art methods for single object tracking.

Original languageEnglish
Pages (from-to)178-187
Number of pages10
JournalSignal Processing
Volume110
DOIs
Publication statusPublished - May 2015

Scopus Subject Areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Occlusion prediction
  • Particle filter
  • Sparse representation
  • Template update
  • Visual object tracking

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