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Robust visual tracking via inverse nonnegative matrix factorization

Fanghui Liu ; Tao Zhou ; Keren Fu (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Jie Yang
41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 20-25 March 2016 (1520-6149). p. 1491-1495. (2016)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: incremental NMF; inverse NMF; local coordinate constraint; visual tracking

Denna post skapades 2016-07-06. Senast ändrad 2017-01-17.
CPL Pubid: 239109


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