{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:05:11Z","timestamp":1753891511799,"version":"3.41.2"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>In recent years, visual tracking algorithms based on Siamese networks have attracted attention for their desirable balance between speed and accuracy. The performance of such tracking methods relies heavily on target templates. Static templates cannot cope with the adverse effects of target appearance change. The dynamic template method, with a template update mechanism, can adapt to the change in target appearance well, but it also causes new problems, which may lead the template to be polluted by noise. Based on the DaSiamRPN and UpdateNet template update networks, a Siamese tracker with \u201cdynamic\u2013static\u201d dual-template fusion and dynamic template adaptive update is proposed in this paper. The new method combines a static template and a dynamic template that is updated in real time for object tracking. An adaptive update strategy was adopted when updating the dynamic template, which can not only help adjust to the changes in the object appearance, but also suppress the adverse effects of noise interference and contamination of the template. The experimental results showed that the robustness and EAO of the proposed method were 23% and 9.0% higher than those of the basic algorithm on the VOT2016 dataset, respectively, and that the precision and success were increased by 0.8 and 0.4% on the OTB100 dataset, respectively. The most comprehensive real-time tracking performance was obtained for the above two large public datasets.<\/jats:p>","DOI":"10.3389\/fnbot.2022.1094892","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T05:15:28Z","timestamp":1673414128000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Siamese tracker with \u201cdynamic\u2013static\u201d dual-template fusion and dynamic template adaptive update"],"prefix":"10.3389","volume":"16","author":[{"given":"Dongyue","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Man","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningdao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxin","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1109\/CVPR.2016.156","article-title":"Staple: Complementary learners for real-time tracking,","author":"Bertinetto","year":"","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition, CVPR"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-48881-3_56","author":"Bertinetto","year":"","journal-title":"Fully-Convolutional Siamese Networks for Object Tracking"},{"key":"B3","article-title":"High performance visual tracking with siamese region proposal network,","author":"Bo","year":"2018","journal-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"B4","doi-asserted-by":"publisher","first-page":"2544","DOI":"10.1109\/CVPR.2010.5539960","article-title":"Visual object tracking using adaptive correlation filters,","author":"Bolme","year":"2010","journal-title":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 13-18, 2010"},{"key":"B5","doi-asserted-by":"publisher","first-page":"6638","DOI":"10.1109\/CVPR.2017.733","article-title":"ECO: Efficient convolution operators for tracking,","author":"Danelljan","year":"","journal-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017"},{"key":"B6","first-page":"58","article-title":"Convolutional features for correlation filter based visual tracking,","volume-title":"Proceedings of the IEEE International Conference on Computer Vision Workshops","author":"Danelljan","year":""},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.490","article-title":"Learning spatially regularized correlation filters for visual tracking,","author":"Danelljan","year":"","journal-title":"2015 IEEE International Conference on Computer Vision (ICCV)"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.159","article-title":"Adaptive decontamination of the training set: A unified formulation for discriminative visual tracking","author":"Danelljan","year":"","journal-title":"IEEE"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","article-title":"Discriminative scale space tracking","volume":"39","author":"Danelljan","year":"","journal-title":"IEEE Trans. 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