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Multi-task non-negative matrix factorization for visual object tracking.
- Source :
- Pattern Analysis & Applications; Feb2020, Vol. 23 Issue 1, p493-507, 15p
- Publication Year :
- 2020
-
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]
Details
- Language :
- English
- ISSN :
- 14337541
- Volume :
- 23
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Pattern Analysis & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 141578684
- Full Text :
- https://doi.org/10.1007/s10044-019-00812-4