Robust visual tracking via inverse nonnegative matrix factorization
Paper i 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


Fanghui Liu

Shanghai Jiao Tong University

Tao Zhou

Shanghai Jiao Tong University

Keren Fu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Jie Yang

Shanghai Jiao Tong University

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

15206149 (ISSN)

978-1-4799-9988-0 (ISBN)







Mer information

Senast uppdaterat