Improved compressive tracking based on pixelwise learner

Ting Chen, Hichem Sahli, Yanning Zhang, Tao Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This work expands upon state-of-the-art multiscale tracking based on compressive sensing (CT) by increasing the overall tracking accuracy. A pixelwise classification stage is incorporated in the CT-based tracker to obtain a relatively stable appearance model, by distinguishing object pixels from the background. In addition, we identify potential distracting regions that are used in a feedback strategy to handle occlusion and avoid drifting toward nearby regions with similar appearances. We evaluate our approach on several benchmark datasets to demonstrate its effectiveness with respect to the state-of-the-art tracking algorithms.

Original languageEnglish
Article number013003
JournalJournal of Electronic Imaging
Volume27
Issue number1
DOIs
StatePublished - 1 Jan 2018

Keywords

  • Compressive tracking
  • feedback strategy
  • pixelwise learner
  • random projection

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