Object tracking via partial least squares analysis

Qing Wang, Feng Chen, Wenli Xu, Ming Hsuan Yang

Research output: Contribution to journalArticle

114 Citations (Scopus)

Abstract

We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.

Original languageEnglish
Article number6224182
Pages (from-to)4454-4465
Number of pages12
JournalIEEE Transactions on Image Processing
Volume21
Issue number10
DOIs
Publication statusPublished - 2012 Sep 28

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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