Robust visual tracking via inverse nonnegative matrix factorization

@article{Liu2015RobustVT,
  title={Robust visual tracking via inverse nonnegative matrix factorization},
  author={Fanghui Liu and Tao Zhou and Keren Fu and Irene Yu-Hua Gu and Jie Yang},
  journal={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2015},
  pages={1491-1495}
}
  • Fanghui LiuTao Zhou Jie Yang
  • Published 20 September 2015
  • Computer Science
  • 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
The establishment of robust target appearance model over time is an overriding concern in visual tracking. In this paper, we propose an inverse nonnegative matrix factorization (NMF) method for robust appearance modeling. Rather than using a linear combination of nonnegative basis vectors for each target image patch in conventional NMF, the proposed method is a reverse thought to conventional NMF tracker. It utilizes both the foreground and background information, and imposes a local coordinate… 

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