  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 matrices for each
target image patch in the 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.
