Dynamic Template Update for Visual Object Tracking

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Siamese-based trackers have recently demonstrated impressive performance and high speed. Despite their great success, conventional siamese trackers are prone to be fooled when facing appearance variations of target objects because they refer to fixed templates captured from first frames to track target objects in the rest of videos. To address this issue, we propose a novel siamese-based tracking framework utilizing a dual template which consists of a static template and a dynamic template. The dynamic template is updated every update interval and allows the tracker to catch appearance variations of the target over time. Furthermore, we introduce a reliability score which prevents incorrect dynamic templates from degrading tracking performance to ensure reliable dynamic template updates. Experimental results show that our method possesses better discriminability and robustness than the baseline, which utilizes a single static template.
Publisher
IEEE
Issue Date
2022-10
Language
English
Citation

IEEE International Conference on Image Processing, ICIP 2022, pp.3111 - 3115

ISSN
1522-4880
DOI
10.1109/ICIP46576.2022.9898009
URI
http://hdl.handle.net/10203/300291
Appears in Collection
EE-Conference Papers(학술회의논문)
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