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- Title
Single target tracking algorithm for lightweight Siamese networks based on global attention.
- Authors
Zhentao WANG; Xiaowei HE; Rao CHENG
- Abstract
Object tracking based on Siamese networks has achieved great success in recent years, but increasingly advanced trackers are also becoming cumbersome, which will severely limit deployment on resource-constrained devices. To solve the above problems, we designed a network with the same or higher tracking performance as other lightweight models based on the SiamFC lightweight tracking model. At the same time, for the problems that the SiamFC tracking network is poor in processing similar semantic information, deformation, illumination change, and scale change, we propose a global attention module and different scale training and testing strategies to solve them. To verify the effectiveness of the proposed algorithm, this paper has done comparative experiments on the ILSVRC, OTB100, VOT2018 datasets. The experimental results show that the method proposed in this paper can significantly improve the performance of the benchmark algorithm.
- Subjects
ARTIFICIAL neural networks; TRACKING algorithms; OBJECT tracking (Computer vision); DEEP learning; PROBLEM solving; ALGORITHMS
- Publication
Bulletin of the Polish Academy of Sciences: Technical Sciences, 2022, Vol 70, Issue 3, p1
- ISSN
0239-7528
- Publication type
Article
- DOI
10.24425/bpasts.2021.139961