Learning correlation filter with fused feature and reliable response for real-time tracking

Bin Lin, Xizhe Xue, Ying Li, Qiang Shen

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)
54 Downloads (Pure)

Abstract

Object tracking is a key component of machine vision system and getting much attention in different walk of life. Recently, correlation filters have been successfully applied to visual tracking. However, how to design effective features and deal with model drifts remain open issues for online tracking. This paper tackles these challenges by proposing a real-time correlation tracking algorithm (RCT) based on two ideas. First, we propose a method to fuse features to more naturally describe the gradient and color information of the tracked object, and introduce the fused feature into a background-aware correlation filter to obtain the response map. Second, we present a novel strategy to significantly reduce noise in the response map and therefore ease the problem of model drift. Systematic comparative evaluations performed over multiple tracking benchmarks demonstrate the efficacy of the proposed approach. The results show that the proposed RCT significantly improves the performance compared to the baseline tracker while still maintaining a real-time tracking speed of 26 fps in MATLAB implementation.

Original languageEnglish
Pages (from-to)417-427
Number of pages11
JournalJournal of Real-Time Image Processing
Volume19
Issue number2
Early online date17 Jan 2022
DOIs
Publication statusPublished - 01 Apr 2022

Keywords

  • Correlation filter
  • Fused feature
  • Model drift
  • Real-time tracking
  • Visual tracking

Fingerprint

Dive into the research topics of 'Learning correlation filter with fused feature and reliable response for real-time tracking'. Together they form a unique fingerprint.

Cite this