Robust visual tracking via inverse nonnegative matrix factorization
Paper in proceeding, 2016

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 constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors. Inverse NMF is used as a feature encoder, where the resulting coefficient vectors are fed into a SVM classifier for separating the target from the background. The proposed method is tested on several videos and compared with seven state-of-the-art methods. Our results have provided further support to the effectiveness and robustness of the proposed method.

visual tracking

local coordinate constraint

inverse NMF

incremental NMF

Author

Fanghui Liu

Shanghai Jiao Tong University

Tao Zhou

Shanghai Jiao Tong University

Keren Fu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Jie Yang

Shanghai Jiao Tong University

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

Vol. 2016-May 1491-1495
978-1-4799-9988-0 (ISBN)

Subject Categories

Signal Processing

DOI

10.1109/ICASSP.2016.7471925

ISBN

978-1-4799-9988-0

More information

Latest update

7/12/2024