VLFSE: Enhancing visual tracking through visual language fusion and state update evaluator
Recently, visual tracking algorithms have achieved impressive results by combining dynamic templates. However, the instability of visual images and the incorrect timing of template updates lead to decreased tracking accuracy and stability in intricate scenarios. To address these issues, we propose a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827024000641 |
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