1. Multi-task non-negative matrix factorization for visual object tracking.
- Author
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Wang, Yong, Luo, Xinbin, Ding, Lu, Fu, Shan, and Hu, Shiqiang
- Subjects
MATRIX decomposition ,NONNEGATIVE matrices ,OBJECT tracking (Computer vision) ,ARTIFICIAL satellite tracking ,TRACKING algorithms - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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