Visual tracking via multi-task non-negative matrix factorization

Yong Wang; Xin-Bin Luo; Shiqiang Hu

ICASSP 2016accepted0 citations

Visual tracking via multi-task non-negative matrix factorization

Abstract

We propose an online tracking algorithm in which the object tracking is achieved by using subspace learning and non-negative matrix factorization (NMF) under the partile filtering framework. The object appearance is modeled by a non-negative combination of non-negative components learned from examples observed in previous frames. In order to robust tracking an object, group sparsity constraints are included to the non-negativity one. In addition, the Alternating Direction Method of Multipliers (ADMM) algorithm is proposed for efficient model updating. Qualitative and quantitative experiments on a variety of challenging sequences show favorable performance of the proposed algorithm against 9 state-of-the-art methods.

BibTeX

@inproceedings{icassp2016_visualtrackingvi,
  title = {Visual tracking via multi-task non-negative matrix factorization},
  author = {Yong Wang and Xin-Bin Luo and Shiqiang Hu},
  booktitle = {ICASSP 2016},
  year = {2016}
}