Incremental Robust Nonnegative Matrix Factorization for Object Tracking

Nonnegative Matrix Factorization NMF has received considerable attention in visual tracking. However noises and outliers are not tackled well due to Frobenius norm in NMF's objective function. To address this issue, in this paper, NMF with $$L_{2,1}$$L2,1 norm loss function robust NMF is introduced into appearance modelling in visual tracking. Compared to standard NMF, robust NMF not only handles noises and outliers but also provides sparsity property. In our visual tracking framework, basis matrix from robust NMF is used for appearance modelling with additional $$\ell _1$$l1 constraint on reconstruction error. The corresponding iterative algorithm is proposed to solve this problem. To strengthen its practicality in visual tracking, multiplicative update rules in incremental learning for robust NMF are proposed for model update. Experiments on the benchmark show that the proposed method achieves favorable performance compared with other state-of-the-art methods.

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