Improved siamese tracking for temporal data association
Yi Tao,
Fei Wang,
Mohan Li,
Jie Liu,
Juncheng Zhou,
Bo Dong,
Ruidong Liu,
Sihao Chen and
Kan Jiao
PLOS ONE, 2025, vol. 20, issue 4, 1-22
Abstract:
Temporal image data association is essential for visual object tracking tasks. This association task is typically stated as a process of connecting signals from the same object at different times along the time axis. Temporal data association is usually performed before state estimation. The accuracy of data association processing results is fundamental to guaranteeing the correctness of all subsequent procedures. This paper proposes an efficient approach for temporal data association focused on obtaining accurate data association processing results in Siamese network framework. Siamese network has recently achieved strong power in visual object tracking owing to its balanced accuracy and speed. Based on data association processing and multi-tracker collaboration, our algorithm achieves high accuracy and strong robustness, which outperforms several state-of-the-art trackers, including standard Siamese trackers.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0320746 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 20746&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0320746
DOI: 10.1371/journal.pone.0320746
Access Statistics for this article
More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().