Inverse Nonnegative Local Coordinate Factorization for Visual Tracking

Recently, nonnegative matrix factorization (NMF) with part-based representation has been widely used for appearance modeling in visual tracking. Unfortunately, not all the targets can be successfully decomposed as “parts” unless some rigorous conditions are satisfied. To avoid this problem, this paper introduces NMF's variants into the visual tracking framework in the view of data clustering for appearance modeling. First, an initial target appearance model based on NMF is proposed to describe the target's appearance with the incorporated local coordinate factorization constraint, orthogonality of the bases, and L
_1,1
norm regularized sparse residual error constraint. Second, an inverse NMF model is proposed in which each learned base vector is regarded as a clustering center in a low-dimensional subspace. Potential target samples (from the foreground) will be clustered around base vectors, while the candidate samples (from the background) are very likely to spread irregularly over the entire clustering space. Such differences can be fully exploited by the inverse NMF model to produce more discriminative encoding vectors than the conventional NMF method. Furthermore, incremental updating model is introduced into the tracking framework for online updating the initial appearance model. Experiments on object tracking benchmark suggest that our tracker is able to achieve promising performance when compared with some state-of-the-art methods in deformation, occlusion, and other challenging situations.