Multiple collaborative kernel tracking

Zhimin Fan*, Ming Yang, Ying Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Those motion parameters that cannot be recovered from image measurements are unobservable in the visual dynamic system. This paper studies this important issue of singularity in the context of kernel-based tracking and presents a novel approach that is based on a motion field representation which employs redundant but sparsely correlated local motion parameters instead of compact but uncorrelated global ones. This approach makes it easy to design fully observable kernel-based motion estimators. This paper shows that these high-dimensional motion fields can be estimated efficiently by the collaboration among a set of simpler local kernel-based motion estimators, which makes the new approach very practical.

Original languageEnglish (US)
Pages (from-to)1268-1273
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume29
Issue number7
DOIs
StatePublished - Jul 2007

Keywords

  • Kernel-based tracking
  • Multiple kernel
  • Visual tracking

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Multiple collaborative kernel tracking'. Together they form a unique fingerprint.

Cite this