Manifold Regularized Correlation Object Tracking

Hongwei Hu, Bo Ma, Jianbing Shen, Ling Shao

Research output: Contribution to journalArticle

30 Citations (Scopus)
4 Downloads (Pure)

Abstract

In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches.
Original languageEnglish
Pages (from-to)1786-1795
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number5
Early online date12 Apr 2017
DOIs
Publication statusPublished - May 2018

Keywords

  • Target tracking
  • Correlation
  • Manifolds
  • Visualization
  • Laplace equations
  • Training

Cite this