Robust object tracking using least absolute deviation

Jingyu Yan, Fuxiang Wang*, Xianbin Cao, Jun Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Recently, sparse representation has been applied to object tracking, where each candidate target is approximately represented as a sparse linear combination of target templates. In this paper, we present a new tracking algorithm, which is faster and more robust than other tracking algorithms, based on sparse representation. First, with an analysis of many typical tracking examples with various degrees of corruption, we model the corruption as a Laplacian distribution. Then, a LAD-Lasso optimisation model is proposed based on Bayesian Maximum A Posteriori (MAP) estimation theory. Compared with L1 Tracker and APG-L1 Tracker, the number of optimisation variables is reduced greatly; it is equal to the number of target templates, regardless of the dimensions of the feature. Finally, we use the Alternating Direction Method of Multipliers (ADMM) to solve the proposed optimisation problem. Experiments on some challenging sequences demonstrate that our proposed method performs better than the state-of-the-art methods in terms of accuracy and robustness.

Original languageEnglish
Pages (from-to)930-939
Number of pages10
JournalImage and Vision Computing
Volume32
Issue number11
DOIs
Publication statusPublished - Nov 2014
Externally publishedYes

Keywords

  • Least absolute deviation
  • Object tracking
  • Sparse representation

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