Correlation Particle Filter for Visual Tracking

Tianzhu Zhang, Si Liu, Changsheng Xu, Bin Liu, Ming Hsuan Yang

Research output: Contribution to journalArticle

66 Citations (Scopus)

Abstract

In this paper, we propose a novel correlation particle filter (CPF) for robust visual tracking. Instead of a simple combination of a correlation filter and a particle filter, we exploit and complement the strength of each one. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has four major advantages: 1) it is robust to partial and total occlusions, and can recover from lost tracks by maintaining multiple hypotheses; 2) it can effectively handle large-scale variation via a particle sampling strategy; 3) it can efficiently maintain multiple modes in the posterior density using fewer particles than conventional particle filters, resulting in low computational cost; and 4) it can shepherd the sampled particles toward the modes of the target state distribution using a mixture of correlation filters, resulting in robust tracking performance. Extensive experimental results on challenging benchmark data sets demonstrate that the proposed CPF tracking algorithm performs favorably against the state-of-the-art methods.

Original languageEnglish
Pages (from-to)2676-2687
Number of pages12
JournalIEEE Transactions on Image Processing
Volume27
Issue number6
DOIs
Publication statusPublished - 2018 Jun

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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