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Multi-task non-negative matrix factorization for visual object tracking
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DOI：10.1007/s10044-019-00812-4.png)
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Abstract
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This paper proposes an online object tracking algorithm in which the object tracking is achieved by using multi-task sparse learning and non-negative matrix factorization under the particle filtering framework. The object appearance is first modeled by subspace learning to reflect the target variations across frames. Combination of non-negative components is learned from examples observed in previous frames. In order to robust tracking an object, group sparsity constraints are included to the non-negativity one. Furthermore, the alternating direction method of multipliers algorithm is employed to compute the model efficiently. Qualitative and quantitative experiments on a variety of challenging sequences show favorable performance of the proposed algorithm against state-of-the-art methods.
Keywords:
Non-negative matrix factorization
Multi-task sparse learning
Alternating direction method of multipliers
Subspace learning
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