{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:44:07Z","timestamp":1767339847734,"version":"3.37.3"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National High-tech Research and Development Program 863, China","award":["2015AA042307"],"award-info":[{"award-number":["2015AA042307"]}]},{"name":"Shandong Provincial Scientific and Technological Development Foundation, China","award":["2014GGX103038"],"award-info":[{"award-number":["2014GGX103038"]}]},{"name":"Shandong Provincial Independent Innovation & Achievement Transformation Special Foundation, China","award":["2015ZDXX0101E01"],"award-info":[{"award-number":["2015ZDXX0101E01"]}]},{"name":"Fundamental Research Funds of Shandong University, China","award":["2015JC027"],"award-info":[{"award-number":["2015JC027"]}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572300"],"award-info":[{"award-number":["61572300"]}],"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":["81871508"],"award-info":[{"award-number":["81871508"]}],"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":["61773246"],"award-info":[{"award-number":["61773246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Taishan Scholar Program of Shandong Province, China","award":["TSHW201502038"],"award-info":[{"award-number":["TSHW201502038"]}]},{"name":"Major Program of Shandong Province Natural Science Foundation, China","award":["ZR2018ZB0419"],"award-info":[{"award-number":["ZR2018ZB0419"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s10489-019-01605-2","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T07:02:58Z","timestamp":1582182178000},"page":"1908-1921","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["End-to-end multitask Siamese network with residual hierarchical attention for real-time object tracking"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5435-8775","authenticated-orcid":false,"given":"Wenhui","family":"Huang","sequence":"first","affiliation":[]},{"given":"Jason","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Yibin","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"issue":"7","key":"1605_CR1","doi-asserted-by":"publisher","first-page":"2415","DOI":"10.1007\/s10489-018-1394-9","volume":"49","author":"H Jiang","year":"2019","unstructured":"Jiang H, Jin W (2019) Effective use of convolutional neural networks and diverse deep supervision for better crowd counting. Appl Intell 49(7):2415\u20132433","journal-title":"Appl Intell"},{"issue":"6","key":"1605_CR2","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1007\/s10489-018-1347-3","volume":"49","author":"G Yao","year":"2019","unstructured":"Yao G, Lei T, Zhong J, Jiang P (2019) Learning multi-temporal-scale deep information for action recognition. Appl Intell 49(6):2017\u20132029","journal-title":"Appl Intell"},{"issue":"1","key":"1605_CR3","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s11042-015-3015-5","volume":"76","author":"Y Zhao","year":"2017","unstructured":"Zhao Y, Xu Z, Xiang Z, Liu Y (2017) Online learning of dynamic multi-view gallery for person re-identification. Multimed Tools Appl 76(1):217\u2013241","journal-title":"Multimed Tools Appl"},{"key":"1605_CR4","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.jvcir.2018.09.019","volume":"56","author":"D Shi","year":"2018","unstructured":"Shi D, Zhu L, Cheng Z, Li Z, Zhang H (2018) Unsupervised multi-view feature extraction with dynamic graph learning. J Vis Commun Image Represent 56:256\u2013264","journal-title":"J Vis Commun Image Represent"},{"issue":"19","key":"1605_CR5","doi-asserted-by":"publisher","first-page":"25475","DOI":"10.1007\/s11042-018-5801-3","volume":"77","author":"S Hou","year":"2018","unstructured":"Hou S, Zhou S, Liu W, Zheng Y (2018) Classifying advertising video by topicalizing high-level semantic concepts. Multimed Tools Appl 77(19):25475\u201325511","journal-title":"Multimed Tools Appl"},{"key":"1605_CR6","doi-asserted-by":"crossref","unstructured":"Huang W, Gu J, Ma X, Li Y (2017) Correlation filter-based self-paced object tracking. In: Proceedings of the IEEE international conference on robotics and automation, pp 4437\u20134442","DOI":"10.1109\/ICRA.2017.7989513"},{"key":"1605_CR7","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1109\/ACCESS.2017.2759583","volume":"6","author":"W Huang","year":"2018","unstructured":"Huang W, Gu J, Ma X, Li Y (2018) Correlation-filter based scale-adaptive visual tracking with hybrid-scheme sample learning. IEEE Access 6:125\u2013137","journal-title":"IEEE Access"},{"issue":"17","key":"1605_CR8","doi-asserted-by":"publisher","first-page":"22247","DOI":"10.1007\/s11042-018-5896-6","volume":"77","author":"B Zhang","year":"2018","unstructured":"Zhang B, Lei Z, Sun J, Zhang H (2018) Cross-media retrieval with collective deep semantic learning. Multimed Tools Appl 77(17):22247\u201322266","journal-title":"Multimed Tools Appl"},{"key":"1605_CR9","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.neucom.2017.01.023","volume":"237","author":"X Sui","year":"2017","unstructured":"Sui X, Zheng Y, Wei B, Bi H, Wu J, Pan X, Yin Y, Zhang S (2017) Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks. Neurocomputing 237:332\u2013341","journal-title":"Neurocomputing"},{"key":"1605_CR10","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In: ECCV Workshop, pp 850\u2013865","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"1605_CR11","doi-asserted-by":"crossref","unstructured":"Valmadre J, Bertinetto L, Henriques JF, Vedaldi A, Torr PHS (2017) End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5000\u20135008","DOI":"10.1109\/CVPR.2017.531"},{"key":"1605_CR12","doi-asserted-by":"crossref","unstructured":"Zhu Z, Wu W, Zou W, Yan J (2018) End-to-end flow correlation tracking with spatial-temporal attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 548\u2013557","DOI":"10.1109\/CVPR.2018.00064"},{"key":"1605_CR13","unstructured":"Wang Q, Gao J, Xing J, Zhang M, Hu W (2017) Dcfnet: discriminant correlation filters network for visual tracking, arXiv:1704.04057"},{"key":"1605_CR14","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 1781\u20131789","DOI":"10.1109\/ICCV.2017.196"},{"key":"1605_CR15","doi-asserted-by":"crossref","unstructured":"Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4293\u20134302","DOI":"10.1109\/CVPR.2016.465"},{"key":"1605_CR16","doi-asserted-by":"crossref","unstructured":"Fan H, Ling H (2017) Sanet: structure-aware network for visual tracking. In: CVPR Workshop, pp 2217\u20132224","DOI":"10.1109\/CVPRW.2017.275"},{"key":"1605_CR17","doi-asserted-by":"crossref","unstructured":"Huang W, Gu J, Ma X, Li Y (2017) Self-paced model learning for robust visual tracking. J Electron Imag, 26(1)","DOI":"10.1117\/1.JEI.26.1.013016"},{"issue":"5","key":"1605_CR18","first-page":"416","volume":"31","author":"W Huang","year":"2016","unstructured":"Huang W, Gu J, Ma X (2016) Compressive sensing with weighted local classifiers for robot visual tracking. Int J Robot Autom 31(5):416\u2013427","journal-title":"Int J Robot Autom"},{"issue":"3","key":"1605_CR19","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2015","unstructured":"Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583\u2013596","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1605_CR20","doi-asserted-by":"crossref","unstructured":"Tang M, Yu B, Zhang F, Wang J (2018) High-speed tracking with multi-kernel correlation filters. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4874\u20134883","DOI":"10.1109\/CVPR.2018.00512"},{"key":"1605_CR21","doi-asserted-by":"crossref","unstructured":"Galoogahi HK, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 1144\u20131152","DOI":"10.1109\/ICCV.2017.129"},{"key":"1605_CR22","doi-asserted-by":"crossref","unstructured":"Mueller M, Smith N, Ghanem B (2017) Context-aware correlation filter tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1387\u20131395","DOI":"10.1109\/CVPR.2017.152"},{"key":"1605_CR23","doi-asserted-by":"crossref","unstructured":"Bibi A, Mueller M, Ghanem B (2016) Target response adaptation for correlation filter tracking. In: Proceedings of the European Conference on Computer Vision, pp 419\u2013433","DOI":"10.1007\/978-3-319-46466-4_25"},{"key":"1605_CR24","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.neucom.2018.12.027","volume":"332","author":"S Xing","year":"2019","unstructured":"Xing S, Liu F, Wang Q, Zhao X, Li T (2019) A hierarchical attention model for rating prediction by leveraging user and product reviews. Neurocomputing 332:417\u2013427","journal-title":"Neurocomputing"},{"key":"1605_CR25","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.neucom.2016.05.098","volume":"213","author":"J Sun","year":"2016","unstructured":"Sun J, Liu X, Wan W, Li J, Zhao D, Zhang H (2016) Video hashing based on appearance and attention features fusion via dbn. Neurocomputing 213:84\u201394","journal-title":"Neurocomputing"},{"key":"1605_CR26","doi-asserted-by":"crossref","unstructured":"Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: Proceedings of the European conference on computer vision, pp 483\u2013499","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"1605_CR27","doi-asserted-by":"crossref","unstructured":"Choi J, Chang HJ, Yun S, Fischer T, Demiris Y, Choi JY (2017) Attentional correlation filter network for adaptive visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4828\u20134837","DOI":"10.1109\/CVPR.2017.513"},{"key":"1605_CR28","doi-asserted-by":"crossref","unstructured":"Ren M, Zemel RS (2017) End-to-end instance segmentation with recurrent attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 293\u2013301","DOI":"10.1109\/CVPR.2017.39"},{"key":"1605_CR29","doi-asserted-by":"crossref","unstructured":"Lukezic A, Vojir T, Cehovin L, Matas J, Kristan M (2017) Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4847\u20134856","DOI":"10.1109\/CVPR.2017.515"},{"key":"1605_CR30","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":"1605_CR31","doi-asserted-by":"crossref","unstructured":"Zhang M, Xing J, Gao J, Hu W (2015) Robust visual tracking using joint scale-spatial correlation filters","DOI":"10.1109\/ICIP.2015.7351044"},{"key":"1605_CR32","doi-asserted-by":"crossref","unstructured":"Chen B, Wang D, Li P, Lu H (2018) Real-time \u2019actor-critic\u2019 tracking. In: Proceedings of the European conference on computer vision, pp 328\u2013345","DOI":"10.1007\/978-3-030-01234-2_20"},{"key":"1605_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Y, Wang L, Wang D, Feng M, Lu H, Qi J (2018) Structured siamese network for real-time visual tracking. In: Proceedings of the European conference on computer vision, pp 355\u2013370","DOI":"10.1007\/978-3-030-01240-3_22"},{"key":"1605_CR34","doi-asserted-by":"crossref","unstructured":"Dong X, Shen J (2018) Triplet loss in siamese network for object tracking. In: Proceedings of the European conference on computer vision, pp 472\u2013488","DOI":"10.1007\/978-3-030-01261-8_28"},{"key":"1605_CR35","doi-asserted-by":"crossref","unstructured":"Wang N, Song Y, Ma C, Zhou W, Liu W, Li H (2019) Unsupervised deep tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.00140"},{"key":"1605_CR36","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PH (2016) Staple: complementary leaners 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":"1605_CR37","doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Khan F, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. In: ICCV Workshop, pp 621\u2013629","DOI":"10.1109\/ICCVW.2015.84"},{"key":"1605_CR38","doi-asserted-by":"crossref","unstructured":"Park E, Berg AC (2018) Meta-tracker: fast and robust online adaptation for visual object trackers. In: Proceedings of the European conference on computer vision, pp 587\u2013604","DOI":"10.1007\/978-3-030-01219-9_35"},{"key":"1605_CR39","doi-asserted-by":"crossref","unstructured":"Zhang T, Xu C, Yang M-H (2017) Multi-task correlation particle filter for robust object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4819\u20134827","DOI":"10.1109\/CVPR.2017.512"},{"key":"1605_CR40","doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Khan FS, 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":"1605_CR41","doi-asserted-by":"crossref","unstructured":"Hong Z, Chen Z, Wang C, Mei X, Prokhorov D, Tao D (2015) Multi-store tracker (muster): a cognitive psychology inspired approach to object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 749\u2013758","DOI":"10.1109\/CVPR.2015.7298675"},{"key":"1605_CR42","doi-asserted-by":"crossref","unstructured":"Choi J, Chang HJ, Yun S, Fischer T, Demiris Y, Choi JY (2017) Attentional correlation filter network for adaptive visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4828\u20134837","DOI":"10.1109\/CVPR.2017.513"},{"key":"1605_CR43","doi-asserted-by":"crossref","unstructured":"Qi Y, Zhang S, Qin L, Yao H, Huang Q, Lim J, Yang M-H (2016) Hedged deep tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4303\u20134311","DOI":"10.1109\/CVPR.2016.466"},{"key":"1605_CR44","doi-asserted-by":"crossref","unstructured":"Tao R, Gavves E, Smeulders AWM (2016) Siamese instance search for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1420\u20131429","DOI":"10.1109\/CVPR.2016.158"},{"key":"1605_CR45","doi-asserted-by":"crossref","unstructured":"Gao J, Zhang T, Xu C (2019) Graph convolutional tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.00478"},{"key":"1605_CR46","doi-asserted-by":"crossref","unstructured":"Wang X, Li C, Luo B, Tang J (2018) Sint++: robust visual tracking via adversarial positive instance generation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4864\u20134873","DOI":"10.1109\/CVPR.2018.00511"},{"key":"1605_CR47","doi-asserted-by":"crossref","unstructured":"Kristan M, Leonardis A, Matas J, Felsberg M et al (2017) The visual object tracking vot2017 challenge results. In: ICCV Workshop, pp 1949\u20131972","DOI":"10.1109\/ICCVW.2017.230"},{"key":"1605_CR48","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Khan FS, Felsberg M (2017) Eco: efficient convolution operators for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6931\u20136939","DOI":"10.1109\/CVPR.2017.733"},{"key":"1605_CR49","doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Khan FS, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: Proceedings of the British machine vision conference","DOI":"10.5244\/C.28.65"},{"key":"1605_CR50","doi-asserted-by":"crossref","unstructured":"Zhang J, Ma S, Sclaroff S (2014) Meem: robust tracking via multiple experts using entropy minimization. In: Proceedings of the European conference on computer vision, pp 188\u2013 203","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"1605_CR51","doi-asserted-by":"crossref","unstructured":"Poostchi M, Palaniappan K, Seetharaman G (2017) Spatial pyramid context-aware moving vehicle detection and tracking in urban aerial imagery. In: Proceedings of the IEEE international conference on advanced video and signal based surveillance, pp 1\u20136","DOI":"10.1109\/AVSS.2017.8078504"},{"key":"1605_CR52","doi-asserted-by":"crossref","unstructured":"Danelljan M, Robinson A, Khan FS, Felsberg M (2016) Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Proceedings of the European conference on computer vision, pp 472\u2013488","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"1605_CR53","doi-asserted-by":"crossref","unstructured":"Wang L, Ouyang W, Wang X, Lu H (2015) Visual tracking with fully convolutional networks. In: Proceedings of the IEEE International conference on computer vision, pp 3119\u2013 3127","DOI":"10.1109\/ICCV.2015.357"},{"key":"1605_CR54","doi-asserted-by":"crossref","unstructured":"Choi J, Chang HJ, Jeong J, Demiris Y, Choi JY (2016) Visual tracking using attention-modulated disintegration and integration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4321\u20134330","DOI":"10.1109\/CVPR.2016.468"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01605-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-019-01605-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01605-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T19:21:14Z","timestamp":1613762474000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-019-01605-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,20]]},"references-count":54,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["1605"],"URL":"https:\/\/doi.org\/10.1007\/s10489-019-01605-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2020,2,20]]},"assertion":[{"value":"20 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}