{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:08:42Z","timestamp":1770491322422,"version":"3.49.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 52171332"],"award-info":[{"award-number":["No. 52171332"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s11063-022-10924-4","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T21:02:29Z","timestamp":1656968549000},"page":"1029-1044","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Siamese Centerness Prediction Network for Real-Time Visual Object Tracking"],"prefix":"10.1007","volume":"55","author":[{"given":"Yue","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3475-6098","authenticated-orcid":false,"given":"Chengtao","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chai Kiat","family":"Yeo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"key":"10924_CR1","doi-asserted-by":"crossref","unstructured":"Yang C, Duraiswami R, Davis L (2005) Efficient mean-shift tracking via a new similarity measure. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 1, pp 176\u2013183 IEEE","DOI":"10.1109\/CVPR.2005.139"},{"issue":"7","key":"10924_CR2","doi-asserted-by":"publisher","first-page":"1772","DOI":"10.1016\/j.patcog.2012.10.006","volume":"46","author":"S Zhang","year":"2013","unstructured":"Zhang S, Yao H, Sun X, Lu X (2013) Sparse coding based visual tracking: Review and experimental comparison. Pattern Recognition 46(7):1772\u20131788","journal-title":"Pattern Recognition"},{"issue":"5","key":"10924_CR3","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1109\/TIP.2014.2311377","volume":"23","author":"J Yu","year":"2014","unstructured":"Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Transactions on Image Processing 23(5):2019\u20132032","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"10924_CR4","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2014","unstructured":"Henriques JF, Caseiro R, Martins P, Batista J (2014) High-speed tracking with kernelized correlation filters. IEEE transactions on pattern analysis and machine intelligence 37(3):583\u2013596","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"issue":"8","key":"10924_CR5","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","volume":"39","author":"M Danelljan","year":"2016","unstructured":"Danelljan M, Hager G, Khan FS, Felsberg M (2016) Discriminative scale space tracking. IEEE transactions on pattern analysis and machine intelligence 39(8):1561\u20131575","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"10924_CR6","doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Shahbaz\u00a0Khan F, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4310\u20134318","DOI":"10.1109\/ICCV.2015.490"},{"key":"10924_CR7","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PH (2016) Staple: Complementary learners for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp 1401\u20131409","DOI":"10.1109\/CVPR.2016.156"},{"key":"10924_CR8","doi-asserted-by":"crossref","unstructured":"Danelljan M, Robinson A, Shahbaz\u00a0Khan F, Felsberg M (2016) Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: European Conference on Computer Vision, Springer, pp 472\u2013488","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"10924_CR9","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Shahbaz\u00a0Khan F, Felsberg M (2017) Eco: Efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6638\u20136646","DOI":"10.1109\/CVPR.2017.733"},{"key":"10924_CR10","doi-asserted-by":"crossref","unstructured":"Bhat G, Johnander J, Danelljan M, Khan FS, Felsberg M (2018) Unveiling the power of deep tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 483\u2013498","DOI":"10.1007\/978-3-030-01216-8_30"},{"issue":"5","key":"10924_CR11","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1002\/int.22814","volume":"37","author":"J Zhang","year":"2022","unstructured":"Zhang J, Yang J, Yu J, Fan J (2022) Semisupervised image classification by mutual learning of multiple self-supervised models. International Journal of Intelligent Systems 37(5):3117\u20133141","journal-title":"International Journal of Intelligent Systems"},{"key":"10924_CR12","unstructured":"Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE transactions on pattern analysis and machine intelligence"},{"key":"10924_CR13","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In: European Conference on Computer Vision, Springer, pp 850\u2013865","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"10924_CR14","doi-asserted-by":"crossref","unstructured":"Wang Q, Teng Z, Xing J, Gao J, Hu W, Maybank S (2018) Learning attentions: residual attentional siamese network for high performance online visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4854\u20134863","DOI":"10.1109\/CVPR.2018.00510"},{"key":"10924_CR15","doi-asserted-by":"crossref","unstructured":"Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8971\u20138980","DOI":"10.1109\/CVPR.2018.00935"},{"key":"10924_CR16","doi-asserted-by":"crossref","unstructured":"Zhu Z, Wang Q, Li B, Wu W, Yan J, Hu W (2018) Distractor-aware siamese networks for visual object tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 101\u2013117","DOI":"10.1007\/978-3-030-01240-3_7"},{"key":"10924_CR17","doi-asserted-by":"crossref","unstructured":"Li B, Wu W, Wang Q, Zhang F, Xing J, Yan J (2019) Siamrpn++: Evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4282\u20134291","DOI":"10.1109\/CVPR.2019.00441"},{"key":"10924_CR18","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhong B, Li G, Zhang S, Ji R (2020) Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6668\u20136677","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"10924_CR19","doi-asserted-by":"publisher","first-page":"12549","DOI":"10.1609\/aaai.v34i07.6944","volume":"34","author":"Y Xu","year":"2020","unstructured":"Xu Y, Wang Z, Li Z, Yuan Y, Yu G (2020) Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines. Proceedings of the AAAI Conference on Artificial Intelligence 34:12549\u201312556","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10924_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106079","volume":"203","author":"K Yang","year":"2020","unstructured":"Yang K, He Z, Zhou Z, Fan N (2020) Siamatt: Siamese attention network for visual tracking. Knowledge-based systems 203:106079","journal-title":"Knowledge-based systems"},{"key":"10924_CR21","doi-asserted-by":"crossref","unstructured":"Guo Q, Feng W, Zhou C, Huang R, Wan L, Wang S (2017) Learning dynamic siamese network for visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1763\u20131771","DOI":"10.1109\/ICCV.2017.196"},{"key":"10924_CR22","doi-asserted-by":"crossref","unstructured":"Wu Y, Lim J, Yang M-H (2013) Online object tracking: A benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2411\u20132418","DOI":"10.1109\/CVPR.2013.312"},{"key":"10924_CR23","doi-asserted-by":"crossref","unstructured":"Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, Cehovin\u00a0Zajc L, Vojir T, Hager G, Lukezic A, Eldesokey A, et\u00a0al (2016) The Visual Object Tracking VOT2016 challenge results. Springer http:\/\/www.springer.com\/gp\/book\/9783319488806","DOI":"10.1007\/978-3-319-48881-3_54"},{"key":"10924_CR24","doi-asserted-by":"crossref","unstructured":"Mueller M, Smith N, Ghanem B (2016) A benchmark and simulator for uav tracking. In: European Conference on Computer Vision, Springer, pp 445\u2013461","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"10924_CR25","doi-asserted-by":"crossref","unstructured":"Fan H, Lin L, Yang F, Chu P, Deng G, Yu S, Bai H, Xu Y, Liao C, Ling H (2019) Lasot: A high-quality benchmark for large-scale single object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5374\u20135383","DOI":"10.1109\/CVPR.2019.00552"},{"key":"10924_CR26","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28"},{"key":"10924_CR27","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25"},{"key":"10924_CR28","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10924_CR29","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"10924_CR30","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"10924_CR31","doi-asserted-by":"crossref","unstructured":"Law H, Deng J (2018) Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 734\u2013750","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"10924_CR32","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2019) Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9627\u20139636","DOI":"10.1109\/ICCV.2019.00972"},{"key":"10924_CR33","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp 315\u2013323. JMLR Workshop and Conference Proceedings"},{"key":"10924_CR34","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122"},{"key":"10924_CR35","doi-asserted-by":"publisher","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2020) Distance-iou loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence 34:12993\u201313000","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10924_CR36","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollar P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European Conference on Computer Vision, Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"3","key":"10924_CR37","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. International journal of computer vision 115(3):211\u2013252","journal-title":"International journal of computer vision"},{"key":"10924_CR38","doi-asserted-by":"crossref","unstructured":"Real E, Shlens J, Mazzocchi S, Pan X, Vanhoucke V (2017) Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5296\u20135305","DOI":"10.1109\/CVPR.2017.789"},{"issue":"5","key":"10924_CR39","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TPAMI.2019.2957464","volume":"43","author":"L Huang","year":"2019","unstructured":"Huang L, Zhao X, Huang K (2019) Got-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(5):1562\u20131577","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10924_CR40","doi-asserted-by":"crossref","unstructured":"Bewley A, Ge Z, Ott L, Ramos F, Upcroft B (2016) Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp 3464\u20133468 IEEE","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"10924_CR41","doi-asserted-by":"crossref","unstructured":"Ning G, Zhang Z, Huang C, Ren X, Wang H, Cai C, He Z (2017) Spatially supervised recurrent convolutional neural networks for visual object tracking. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp 1\u20134. IEEE","DOI":"10.1109\/ISCAS.2017.8050867"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10924-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-10924-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10924-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T12:40:37Z","timestamp":1727527237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-10924-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,4]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["10924"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-10924-4","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,4]]},"assertion":[{"value":"7 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}