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Lightweight Target-Aware Attention Learning Network-Based Target Tracking Method.
- Source :
- Mathematics (2227-7390); Jul2022, Vol. 10 Issue 13, p2299-N.PAG, 18p
- Publication Year :
- 2022
-
Abstract
- Siamese network trackers based on pre-trained depth features have achieved good performance in recent years. However, the pre-trained depth features are trained in advance on large-scale datasets, which contain feature information of a large number of objects. There may be a pair of interference and redundant information for a single tracking target. To learn a more accurate target feature information, this paper proposes a lightweight target-aware attention learning network to learn the most effective channel features of the target online. The lightweight network uses a designed attention learning loss function to learn a series of channel features with weights online with no complex parameters. Compared with the pre-trained features, the channel features with weights can represent the target more accurately. Finally, the lightweight target-aware attention learning network is unified into a Siamese tracking network framework to implement target tracking effectively. Experiments on several datasets demonstrate that the tracker proposed in this paper has good performance. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARTIFICIAL neural networks
TRACKING radar
Subjects
Details
- Language :
- English
- ISSN :
- 22277390
- Volume :
- 10
- Issue :
- 13
- Database :
- Complementary Index
- Journal :
- Mathematics (2227-7390)
- Publication Type :
- Academic Journal
- Accession number :
- 157999338
- Full Text :
- https://doi.org/10.3390/math10132299