Robust object tracking using least absolute deviation

作者：

Highlights：

• The representation error is modelled as a Laplacian distribution.

• We derive our new LAD–Lasso model based on a Bayesian MAP estimate.

• LAD–Lasso model is robust to outliers.

• The number of optimisation variable in the new model reduces greatly.

• We use ADMM algorithm to solve the new optimisation problem.

摘要

•The representation error is modelled as a Laplacian distribution.•We derive our new LAD–Lasso model based on a Bayesian MAP estimate.•LAD–Lasso model is robust to outliers.•The number of optimisation variable in the new model reduces greatly.•We use ADMM algorithm to solve the new optimisation problem.

论文关键词：Object tracking,Sparse representation,Least absolute deviation

论文评审过程：Received 6 August 2013, Revised 10 March 2014, Accepted 28 August 2014, Available online 6 September 2014.

论文官网地址：https://doi.org/10.1016/j.imavis.2014.08.008