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Object tracking via dense SIFT features and low-rank representation.
- Source :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications; Oct2019, Vol. 23 Issue 20, p10173-10186, 14p
- Publication Year :
- 2019
-
Abstract
- In this paper, we present a low-rank sparse tracking method which builds upon the particle filtering framework. The proposed method learns the local dense scale-invariant feature transform features corresponding to candidate samples jointly by exploiting the underlying sparse and low-rank constraints. Furthermore, the alternating direction method of multipliers method guarantees the optimization equation can be solved accurately and robustly. We evaluate our proposed tracking method against 9 state-of-the-art trackers on a set of 64 challenging sequences. Experimental results show that the proposed method performs favorably against state-of-the-art trackers in terms of accuracy. [ABSTRACT FROM AUTHOR]
- Subjects :
- OBJECT tracking (Computer vision)
ARTIFICIAL satellite tracking
Subjects
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 23
- Issue :
- 20
- Database :
- Complementary Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 138725356
- Full Text :
- https://doi.org/10.1007/s00500-018-3571-5