{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:02:20Z","timestamp":1740153740506,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key R&D Program of China","award":["2017YFB0502900"],"award-info":[{"award-number":["2017YFB0502900"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702498"],"award-info":[{"award-number":["61702498"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAS \u201dLight of West China\u201d Program","award":["XAB2017B15"],"award-info":[{"award-number":["XAB2017B15"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s12559-020-09741-5","type":"journal-article","created":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T06:08:34Z","timestamp":1596175714000},"page":"1593-1602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multiple Reliable Structured Patches for Object Tracking"],"prefix":"10.1007","volume":"13","author":[{"given":"Siyuan","family":"Wu","sequence":"first","affiliation":[]},{"given":"Ju","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6874-3236","authenticated-orcid":false,"given":"Yachuang","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Bangyong","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,31]]},"reference":[{"key":"9741_CR1","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.neucom.2018.01.068","volume":"286","author":"B Bai","year":"2018","unstructured":"Bai B, Zhong B, Ouyang G, Wang P, Liu X, Chen Z, Wang C. Kernel correlation filters for visual tracking with adaptive fusion of heterogeneous cues. Neurocomputing 2018;286:109\u2013120.","journal-title":"Neurocomputing"},{"key":"9741_CR2","doi-asserted-by":"crossref","unstructured":"Bhat G, Danelljan M, Van Gool L, Timofte R. 2019. Learning discriminative model prediction for tracking. arXiv:1904.07220.","DOI":"10.1109\/ICCV.2019.00628"},{"key":"9741_CR3","doi-asserted-by":"crossref","unstructured":"Choi J, Kwon J, Lee KM. Deep meta learning for real-time target-aware visual tracking. Proceedings of the IEEE International Conference on Computer Vision; 2019. p. 911\u2013920.","DOI":"10.1109\/ICCV.2019.00100"},{"key":"9741_CR4","doi-asserted-by":"crossref","unstructured":"Dai K, Wang D, Lu H, Sun C, Li J. Visual tracking via adaptive spatially-regularized correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2019. p. 4670\u20134679.","DOI":"10.1109\/CVPR.2019.00480"},{"key":"9741_CR5","doi-asserted-by":"crossref","unstructured":"Danelljan M, H\u00e4ger G., Khan F, Felsberg M. Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference; 2014.","DOI":"10.5244\/C.28.65"},{"key":"9741_CR6","doi-asserted-by":"crossref","unstructured":"Dinh TB, Vo N, Medioni G. Context tracker: exploring supporters and distracters in unconstrained environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2011. p. 1177\u20131184.","DOI":"10.1109\/CVPR.2011.5995733"},{"key":"9741_CR7","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.neucom.2019.08.005","volume":"367","author":"Y Fang","year":"2019","unstructured":"Fang Y, Ko S, Jo GS. Robust visual tracking based on global-and-local search with confidence reliability estimation. Neurocomputing 2019;367:273\u2013286.","journal-title":"Neurocomputing"},{"key":"9741_CR8","doi-asserted-by":"crossref","unstructured":"Gao J, Ling H, Hu W, Xing J. Transfer learning based visual tracking with Gaussian processes regression. In: Proceedings of the European Conference on Computer Vision; 2014. p. 188\u2013 203.","DOI":"10.1007\/978-3-319-10578-9_13"},{"issue":"8","key":"9741_CR9","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1049\/iet-ipr.2017.0443","volume":"12","author":"Z Hao","year":"2018","unstructured":"Hao Z, Liu G, Zhang H. Correlation filter-based visual tracking via adaptive weighted CNN features fusion. IET Image Process 2018;12(8):1423\u20131431.","journal-title":"IET Image Process"},{"issue":"3","key":"9741_CR10","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. High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 2014;37(3):583\u2013596.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9741_CR11","unstructured":"Hong S, You T, Kwak S, Han B. Online tracking by learning discriminative saliency map with convolutional neural network. In: Proceedings of the International Conference on Machine Learning; 2015. p. 597\u2013606."},{"key":"9741_CR12","doi-asserted-by":"crossref","unstructured":"Huang Y, Ju C, Hu X, Ci W. An anti-occlusion and scale adaptive kernel correlation filter for visual object tracking. KSII Transactions on Internet & Information Systems. 2019;13(4).","DOI":"10.3837\/tiis.2019.04.020"},{"key":"9741_CR13","unstructured":"Jia X, Lu H, Yang MH. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2012. p. 1822\u20131829."},{"issue":"11","key":"9741_CR14","doi-asserted-by":"publisher","first-page":"2770","DOI":"10.1109\/TPAMI.2018.2864965","volume":"41","author":"C Li","year":"2018","unstructured":"Li C, Lin L, Zuo W, Tang J, Yang MH. Visual tracking via dynamic graph learning. IEEE Trans Pattern Anal Mach Intell 2018;41(11):2770\u20132782.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9741_CR15","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.jvcir.2018.09.004","volume":"56","author":"G Li","year":"2018","unstructured":"Li G, Peng M, Nai K, Li Z, Li K. Visual tracking via context-aware local sparse appearance model. J Vis Commun Image Represent 2018;56:92\u2013105.","journal-title":"J Vis Commun Image Represent"},{"key":"9741_CR16","doi-asserted-by":"crossref","unstructured":"Li P, Chen B, Ouyang W, Wang D, Yang X, Lu H. Gradnet: Gradient-guided network for visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision; 2019. p. 6162\u20136171.","DOI":"10.1109\/ICCV.2019.00626"},{"issue":"4","key":"9741_CR17","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/2508037.2508039","volume":"4","author":"X Li","year":"2013","unstructured":"Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel AVD. A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 2013;4(4):58.","journal-title":"ACM Trans Intell Syst Technol"},{"key":"9741_CR18","doi-asserted-by":"crossref","unstructured":"Li Y, Zhu J, Hoi SC, Song W, Wang Z, Liu H. 2019. Robust estimation of similarity transformation for visual object tracking, Vol. 33.","DOI":"10.1609\/aaai.v33i01.33018666"},{"issue":"12","key":"9741_CR19","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1049\/iet-ipr.2018.6517","volume":"13","author":"YQ Lv","year":"2019","unstructured":"Lv YQ, Liu K, Cheng F, Li W. Visual tracking with tree-structured appearance model for online learning. IET Image Process 2019;13(12):2106\u20132115.","journal-title":"IET Image Process"},{"issue":"11","key":"9741_CR20","doi-asserted-by":"publisher","first-page":"2855","DOI":"10.1587\/transinf.2018EDL8116","volume":"101","author":"J Pi","year":"2018","unstructured":"Pi J, Zeng S, Zuo Q, Wei Y. Accurate scale adaptive and real-time visual tracking with correlation filters. IEICE Trans Inf Syst 2018;101(11):2855\u20132858.","journal-title":"IEICE Trans Inf Syst"},{"key":"9741_CR21","unstructured":"Pu S, Song Y, Ma C, Zhang H, Yang MH. Deep attentive tracking via reciprocative learning. In: Advances in neural information processing systems; 2018. p. 1931\u20131941."},{"issue":"3","key":"9741_CR22","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/s11263-016-0908-3","volume":"120","author":"J Revaud","year":"2016","unstructured":"Revaud J, Weinzaepfel P, Harchaoui Z, Schmid C. Deepmatching: Hierarchical deformable dense matching. Int J Comput Vis 2016;120(3):300\u2013323.","journal-title":"Int J Comput Vis"},{"issue":"7","key":"9741_CR23","first-page":"1442","volume":"36","author":"AW Smeulders","year":"2013","unstructured":"Smeulders AW, Chu DM, Cucchiara R, Calderara S, Dehghan A, Shah M. Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 2013;36(7):1442\u20131468.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9741_CR24","unstructured":"Sui Y, Zhang Z, Wang G, Tang Y, Zhang L. Exploiting the anisotropy of correlation filter learning for visual tracking. International Journal of Computer Vision. 2019;1\u201322."},{"issue":"2","key":"9741_CR25","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s11760-018-1364-z","volume":"13","author":"DE Touil","year":"2019","unstructured":"Touil DE, Terki N, Medouakh S. Hierarchical convolutional features for visual tracking via two combined color spaces with svm classifier. SIViP 2019;13(2):359\u2013368.","journal-title":"SIViP"},{"issue":"10","key":"9741_CR26","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.3390\/s18103513","volume":"18","author":"GJ Yoon","year":"2018","unstructured":"Yoon GJ, Hwang H, Yoon S. Visual object tracking using structured sparse PCA-based appearance representation and online learning. Sensors 2018;18(10):3513.","journal-title":"Sensors"},{"key":"9741_CR27","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.imavis.2019.07.008","volume":"89","author":"B Zhang","year":"2019","unstructured":"Zhang B, Shao X, Chen W, Bi F, Fang W, Sun T, Tang C. Visual tracking based on robust appearance model. Image Vis Comput 2019;89:211\u2013221.","journal-title":"Image Vis Comput"},{"key":"9741_CR28","doi-asserted-by":"crossref","unstructured":"Zhang J, Ma S, Sclaroff S. Meem: robust tracking via multiple experts using entropy minimization. In: Proceedings of the European Conference on Computer Vision; 2014. p. 188\u2013203.","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"9741_CR29","doi-asserted-by":"crossref","unstructured":"Zhang M, Wang Q, Xing J, Gao J, Peng P, Hu W, Maybank S. Visual tracking via spatially aligned correlation filters network. In: Proceedings of the European Conference on Computer Vision; 2018. p. 469\u2013485.","DOI":"10.1007\/978-3-030-01219-9_29"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-020-09741-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-020-09741-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-020-09741-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,11]],"date-time":"2021-12-11T08:07:05Z","timestamp":1639210025000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-020-09741-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,31]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["9741"],"URL":"https:\/\/doi.org\/10.1007\/s12559-020-09741-5","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"type":"print","value":"1866-9956"},{"type":"electronic","value":"1866-9964"}],"subject":[],"published":{"date-parts":[[2020,7,31]]},"assertion":[{"value":"28 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interest"}}]}}