SiamMMF: multi-modal multi-level fusion object tracking based on Siamese networks-Reference-Cited by-同舟云学术

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SiamMMF: multi-modal multi-level fusion object tracking based on Siamese networks

Published:2022-12-05 Issue:1 Volume:34 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Yang Zhen,Huang Peng,He Dunyun,Cai Zhongwang,Yin Zhijian

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province

Education Department of Jiangxi Province

the Youth Top Talent Foundation of Jiangxi Science and Technology Normal University

Key Laboratory of System Control and Information Processing

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s00138-022-01354-2.pdf

Reference52 articles.

1. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: Proceedings of the 2006 British Machine Vision, pp. 47–56 (2006)

2. Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)

3. Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: European Conference on Computer Vision, pp. 234–247 (2008)

4. Zhang, J. M., Ma, S.G., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: European Conference on Computer Vision, pp. 188–203 (2014)

5. Hare, S., Golodetz, S., Saffari, A.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2015)

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