{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:30:29Z","timestamp":1772166629676,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62076201"],"award-info":[{"award-number":["No.62076201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1934222"],"award-info":[{"award-number":["U1934222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Image Video Proc."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>SiamFC++ only extracts the object feature of the first frame as a tracking template, and only uses the highest level feature maps in both the classification branch and the regression branch, so that the respective characteristics of the two branches are not fully utilized. In view of this, the present paper proposes an object tracking algorithm based on SiamFC++. The algorithm uses the multi-layer features of the Siamese network to update template. First, FPN is used to extract feature maps from different layers of Backbone for classification branch and regression branch. Second, 3D convolution is used to update the tracking template of the object tracking algorithm. Next, a template update judgment condition is proposed based on mutual information. Finally, AlexNet is used as the backbone and GOT-10K as training set. Compared with SiamFC++, our algorithm obtains improved results on OTB100, VOT2016, VOT2018 and GOT-10k data sets, and the tracking process is real time.<\/jats:p>","DOI":"10.1186\/s13640-023-00616-x","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T09:02:06Z","timestamp":1704358926000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-layer features template update object tracking algorithm based on SiamFC++"],"prefix":"10.1186","volume":"2024","author":[{"given":"Xiaofeng","family":"Lu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3991-9976","authenticated-orcid":false,"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhengyang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xinhong","family":"Hei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"issue":"3","key":"616_CR1","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.rti.2005.03.006","volume":"11","author":"J Shin","year":"2005","unstructured":"J. Shin, S. Kim, S. Kang, S. Lee, J.K. Paik, B.R. Abidi, M.A. Abidi, Optical flow-based real-time object tracking using non-prior training active feature model. Real Time Imaging 11(3), 204\u2013218 (2005). https:\/\/doi.org\/10.1016\/j.rti.2005.03.006","journal-title":"Real Time Imaging"},{"key":"616_CR2","doi-asserted-by":"publisher","unstructured":"S. Spors, R. Rabenstein, A real-time face tracker for color video. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2001, 7-11 May, 2001, Salt Palace Convention Center, Salt Lake City, Utah, USA, Proceedings, pp. 1493\u20131496 (2001). https:\/\/doi.org\/10.1109\/ICASSP.2001.941214","DOI":"10.1109\/ICASSP.2001.941214"},{"issue":"3","key":"616_CR3","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1023\/A:1008935410038","volume":"10","author":"A Doucet","year":"2000","unstructured":"A. Doucet, S.J. Godsill, C. Andrieu, On sequential monte Carlo sampling methods for Bayesian filtering. Stat. Comput. 10(3), 197\u2013208 (2000). https:\/\/doi.org\/10.1023\/A:1008935410038","journal-title":"Stat. Comput."},{"key":"616_CR4","doi-asserted-by":"publisher","unstructured":"D.S. Bolme, B.A. Draper, J.R. Beveridge, Average of synthetic exact filters. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA, pp. 2105\u20132112 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206701","DOI":"10.1109\/CVPR.2009.5206701"},{"key":"616_CR5","doi-asserted-by":"publisher","unstructured":"J.F. Henriques, R. Caseiro, P. Martins, J.P. Batista, Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A.W., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) Computer Vision - ECCV 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV. Lecture Notes in Computer Science, vol. 7575, pp. 702\u2013715 (2012). https:\/\/doi.org\/10.1007\/978-3-642-33765-9_50","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"616_CR6","doi-asserted-by":"publisher","unstructured":"R. Tao, E. Gavves, A.W.M. Smeulders, Siamese instance search for tracking. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pp. 1420\u20131429 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.158","DOI":"10.1109\/CVPR.2016.158"},{"key":"616_CR7","doi-asserted-by":"publisher","unstructured":"L. Bertinetto, J. Valmadre, J.F. Henriques, A. Vedaldi, P.H.S. Torr, Fully-convolutional siamese networks for object tracking. In: Hua, G., J\u00e9gou, H. (eds.) Computer Vision - ECCV 2016 Workshops - Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II. Lecture Notes in Computer Science, vol. 9914, pp. 850\u2013865 (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_56","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"616_CR8","doi-asserted-by":"publisher","unstructured":"B. Li, J. Yan, W.Wu, Z. Zhu, , X. Hu, High performance visual tracking with siamese region proposal network. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 8971\u20138980 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00935","DOI":"10.1109\/CVPR.2018.00935"},{"key":"616_CR9","doi-asserted-by":"publisher","unstructured":"Q. Wang, L. Zhang, L. Bertinetto, W. Hu, P.H.S. Torr, Fast online object tracking and segmentation: a unifying approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 1328\u20131338 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00142","DOI":"10.1109\/CVPR.2019.00142"},{"key":"616_CR10","doi-asserted-by":"publisher","unstructured":"Z. Zhang, H.Peng, Deeper and wider siamese networks for real-time visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 4591\u20134600 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00472","DOI":"10.1109\/CVPR.2019.00472"},{"key":"616_CR11","doi-asserted-by":"publisher","unstructured":"B. Li , W. Wu, Q. Wang, F. Zhang, J. Xing, J. Yan, Siamrpn++: evolution of siamese visual tracking with very deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 4282\u20134291 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00441","DOI":"10.1109\/CVPR.2019.00441"},{"key":"616_CR12","doi-asserted-by":"crossref","unstructured":"Y. Xu, Z. Wang, Z., Li, Y. Ye, G. Yu, Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 12549\u201312556 (2020)","DOI":"10.1609\/aaai.v34i07.6944"},{"key":"616_CR13","doi-asserted-by":"publisher","unstructured":"Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, N. Yu, Online multi-object tracking using cnn-based single object tracker with spatial-temporal attention mechanism. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 4846\u20134855 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.518","DOI":"10.1109\/ICCV.2017.518"},{"key":"616_CR14","doi-asserted-by":"publisher","unstructured":"A. Sadeghian, A. Alahi, S. Savarese, Tracking the untrackable: learning to track multiple cues with long-term dependencies. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 300\u2013311 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.41","DOI":"10.1109\/ICCV.2017.41"},{"issue":"9","key":"616_CR15","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Y. Wu, J. Lim, M. Yang, Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834\u20131848 (2015). https:\/\/doi.org\/10.1109\/TPAMI.2014.2388226","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"616_CR16","doi-asserted-by":"publisher","unstructured":"M. Kristan, A. Leonardis, J. Matas, The visual object tracking VOT2016 challenge results. In: Hua, G., J\u00e9gou, H. (eds.) Computer Vision - ECCV 2016 Workshops - Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II. Lecture Notes in Computer Science, vol. 9914, pp. 777\u2013823 (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_54","DOI":"10.1007\/978-3-319-48881-3_54"},{"key":"616_CR17","doi-asserted-by":"publisher","unstructured":"M. Kristan, A. Leonardis, J. Matas, The sixth visual object tracking VOT2018 challenge results. In: Leal-Taix\u00e9, L., Roth, S. (eds.) Computer Vision - ECCV 2018 Workshops - Munich, Germany, September 8-14, 2018, Proceedings, Part I. Lecture Notes in Computer Science, vol. 11129, pp. 3\u201353 (2018). https:\/\/doi.org\/10.1007\/978-3-030-11009-3_1","DOI":"10.1007\/978-3-030-11009-3_1"},{"key":"616_CR18","unstructured":"L. Huang, X. Zhao, K. Huang, Got-10k: A large high-diversity benchmark for generic object tracking in the wild. CoRR arXiv: abs\/1810.11981 (2018)"},{"key":"616_CR19","doi-asserted-by":"publisher","unstructured":"J. Valmadre, L. Bertinetto, J.F. Henriques, A. Vedaldi, P.H.S. Torr, End-to-end representation learning for correlation filter based tracking. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pp. 5000\u20135008 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.531","DOI":"10.1109\/CVPR.2017.531"},{"key":"616_CR20","doi-asserted-by":"publisher","unstructured":"T. Lin, P. Doll\u00e1r, R.B. Girshick, K. He, B. Hariharan, S.J. Belongie, Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pp. 936\u2013944 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"issue":"6","key":"616_CR21","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"S. Ren, K. He, R.B. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"616_CR22","doi-asserted-by":"crossref","unstructured":"A.K Mirabadi, S. Rini, The information & mutual information ratio for counting image features and their matches. CoRR arXiv:abs\/2005.06739 (2020)","DOI":"10.1109\/IWCIT50667.2020.9163458"},{"issue":"1","key":"616_CR23","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/s10489-011-0315-y","volume":"37","author":"LT Vinh","year":"2012","unstructured":"L.T. Vinh, S. Lee, Y. Park, B.J. d\u2019Auriol, A novel feature selection method based on normalized mutual information. Appl. Intell. 37(1), 100\u2013120 (2012). https:\/\/doi.org\/10.1007\/s10489-011-0315-y","journal-title":"Appl. Intell."},{"key":"616_CR24","doi-asserted-by":"publisher","unstructured":"N.D. Binh, Online multiple tasks one-shot learning of object categories and vision. In: Taniar, D., Pardede, E., Nguyen, H., Rahayu, J.W., Khalil, I. (eds.) MoMM\u20192011 - The Nineth International Conference on Advances in Mobile Computing and Multimedia, 5-7 December 2011, Ho Chi Minh City, Vietnam, pp. 131\u2013138 (2011). https:\/\/doi.org\/10.1145\/2095697.2095722","DOI":"10.1145\/2095697.2095722"},{"key":"616_CR25","unstructured":"A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need. Adv. Neural. Inf. Process. Syst. (2017). https:\/\/doi.org\/10.48550\/arXiv.1706.03762"},{"key":"616_CR26","doi-asserted-by":"publisher","unstructured":"M. Danelljan, G. Bhat, F.S Khan, M. Felsberg, ECO: efficient convolution operators for tracking. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pp. 6931\u20136939 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.733","DOI":"10.1109\/CVPR.2017.733"},{"key":"616_CR27","doi-asserted-by":"crossref","unstructured":"T. Yang, P. Xu, R. Hu, H.Chai, A.B. Chan, Roam: Recurrently optimizing tracking model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6718\u20136727 (2020)","DOI":"10.1109\/CVPR42600.2020.00675"},{"key":"616_CR28","doi-asserted-by":"publisher","first-page":"4814","DOI":"10.1109\/TIP.2021.3076272","volume":"30","author":"S Yao","year":"2021","unstructured":"S. Yao, X. Han, H. Zhang, X. Wang, X. Cao, Learning deep Lucas-Kanade Siamese network for visual tracking. IEEE Trans. Image Process. 30, 4814\u20134827 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"616_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01185-7","volume":"32","author":"Z Li","year":"2021","unstructured":"Z. Li, G.-A. Bilodeau, W. Bouachir, Multiple convolutional features in Siamese networks for object tracking. Mach. Vis. Appl. 32, 1\u201311 (2021)","journal-title":"Mach. Vis. Appl."},{"issue":"9","key":"616_CR30","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.3390\/electronics10091067","volume":"10","author":"T Yuan","year":"2021","unstructured":"T. Yuan, W. Yang, Q. Li, Y. Wang, An anchor-free Siamese network with multi-template update for object tracking. Electronics 10(9), 1067 (2021)","journal-title":"Electronics"}],"container-title":["EURASIP Journal on Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-023-00616-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13640-023-00616-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-023-00616-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T09:07:35Z","timestamp":1704359255000},"score":1,"resource":{"primary":{"URL":"https:\/\/jivp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13640-023-00616-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,4]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["616"],"URL":"https:\/\/doi.org\/10.1186\/s13640-023-00616-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1265689\/v1","asserted-by":"object"}]},"ISSN":["1687-5281"],"issn-type":[{"value":"1687-5281","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,4]]},"assertion":[{"value":"25 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Consent for publication","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not application","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"We have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}