{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:40Z","timestamp":1760245060871},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"29-30","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,8]]},"DOI":"10.1007\/s11042-020-08839-0","type":"journal-article","created":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T14:04:50Z","timestamp":1587391490000},"page":"20521-20543","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A target response adaptive correlation filter tracker with spatial attention"],"prefix":"10.1007","volume":"79","author":[{"given":"Yifei","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yafu","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"issue":"8","key":"8839_CR1","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","volume":"33","author":"B Babenko","year":"2011","unstructured":"Babenko B, Yang M, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619\u20131632","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"8839_CR2","first-page":"2764","volume":"86","author":"A Behrad","year":"2003","unstructured":"Behrad A, Motamedi SA (2003) Moving target detection and tracking using edge features detection and matching. IEICE Trans Inf Syst 86(12):2764\u20132774","journal-title":"IEICE Trans Inf Syst"},{"key":"8839_CR3","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PHS (2016) Staple: Complementary learners for real-time tracking. In: The IEEE conference on computer vision and pattern recognition. CVPR","DOI":"10.1109\/CVPR.2016.156"},{"key":"8839_CR4","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS (2016) Fully-convolutional siamese networks for object tracking. In: Proceedings of the european conference on computer vision workshops, pp 850\u2013865","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"8839_CR5","doi-asserted-by":"crossref","unstructured":"Bibi A, Mueller M, Ghanem B (2016) Target response adaptation for correlation filter tracking. ECCV","DOI":"10.1007\/978-3-319-46466-4_25"},{"key":"8839_CR6","doi-asserted-by":"crossref","unstructured":"Bolme DS, Beveridge JR, Draper B, Lui YM et al (2010) Visual object tracking using adaptive correlation filters. In: IEEE conference on computer vision and pattern recognition. CVPR","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"8839_CR7","unstructured":"Cannons K (2008) A review of visual tracking. Technical Report CSE 2008-07, York University, Canada"},{"key":"8839_CR8","doi-asserted-by":"crossref","unstructured":"Choi J, Chang HJ, Yun S, Fischer T, Demiris Y (2017) Attentional correlation filter network for adaptive visual tracking. In: IEEE conference on computer vision and pattern recognition, pp 4807\u20134816, 2, 7","DOI":"10.1109\/CVPR.2017.513"},{"key":"8839_CR9","doi-asserted-by":"crossref","unstructured":"Choi J, Jin Chang H, Jeong J et al (2016) Visual tracking using attention-modulated disintegration and integration[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4321-4330","DOI":"10.1109\/CVPR.2016.468"},{"key":"8839_CR10","doi-asserted-by":"crossref","unstructured":"Danelljan M, H\u00e4ger G, Khan F, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. ICCV workshop","DOI":"10.1109\/ICCVW.2015.84"},{"key":"8839_CR11","doi-asserted-by":"crossref","unstructured":"Danelljan M, H\u00e4ger G, Khan F, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. ICCV workshop","DOI":"10.1109\/ICCVW.2015.84"},{"key":"8839_CR12","doi-asserted-by":"crossref","unstructured":"Danelljan M, H\u00e4ger G, Khan FS, Felsberg M (2014) Accurate scale estimation for robust visual tracking. BMVC","DOI":"10.5244\/C.28.65"},{"key":"8839_CR13","doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Shahbaz Khan F et al (2015) Learning spatially regularized correlation filters for visual tracking [C]. ICCV","DOI":"10.1109\/ICCV.2015.490"},{"key":"8839_CR14","doi-asserted-by":"crossref","unstructured":"Fan DP, Cheng MM, Liu JJ et al (2018) Salient objects in clutter: bringing salient object detection to the foreground[C]. In: Proceedings of the European conference on computer vision (ECCV), pp 186\u2013202","DOI":"10.1007\/978-3-030-01267-0_12"},{"key":"8839_CR15","doi-asserted-by":"crossref","unstructured":"Fan DP, Wang W, Cheng MM et al (2019) Shifting more attention to video salient object detection[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8554\u20138564","DOI":"10.1109\/CVPR.2019.00875"},{"key":"8839_CR16","doi-asserted-by":"crossref","unstructured":"Gao L, Wu j, Qiao z, et al. (2016) Collaborative social group influence for event recommendation[C]. ACM International. ACM","DOI":"10.1145\/2983323.2983879"},{"key":"8839_CR17","doi-asserted-by":"crossref","unstructured":"Gao L, Zhou C, Wu J et al (2017) Collaborative dynamic sparse topic regression with user profile evolution for item recommendation[C]. In: The thirty-first conference on artificial intelligence, AAAI-17","DOI":"10.1609\/aaai.v31i1.10726"},{"issue":"10","key":"8839_CR18","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1109\/TPAMI.2015.2509974","volume":"38","author":"S Hare","year":"2016","unstructured":"Hare S, Golodetz S, Saffari A, Vineet V, Cheng MM, Hicks SL, Torr PHS (2016) Struck: Structured output tracking with kernels. IEEE Trans Pattern Anal Mach Intell 38(10):2096\u20132109","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8839_CR19","doi-asserted-by":"crossref","unstructured":"Henriques JF, Caseiro R, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: European conference on computer vision, pp 702\u2013715","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"8839_CR20","doi-asserted-by":"crossref","unstructured":"Henriques JF, Caseiro R, Martins P, Batista J, High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI (2015)","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"8839_CR21","doi-asserted-by":"crossref","unstructured":"Jamasbi B, Motamedi SA, Behrad A (2007) Tracking vehicle targets with large aspect change. In: 2007 IEEE Workshop on Motion and Video Computing (WMVC\u201907), pp 22\u201322. IEEE","DOI":"10.1109\/WMVC.2007.37"},{"issue":"1","key":"8839_CR22","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1115\/1.3662552","volume":"82","author":"RE Kalman","year":"1960","unstructured":"Kalman RE (1960) A new approach to linear filtering and prediction problems[J]. J Basic Eng 82(1):35\u201345","journal-title":"J Basic Eng"},{"key":"8839_CR23","doi-asserted-by":"crossref","unstructured":"Kiani Galoogahi H, Sim T, Lucey S (2015) Correlation filters with limited boundaries [C]. CVPR","DOI":"10.1109\/CVPR.2015.7299094"},{"key":"8839_CR24","unstructured":"Kristan M, Leonardis A, Matas J et al (2016) The visual object tracking vot2016 challenge results. In: Proceedings of the european conference on computer vision workshops, pp 1\u201345.2, 7, 8"},{"key":"8839_CR25","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: CVPR. 1, 2, 3, 4, 5, 8","DOI":"10.1109\/CVPR.2018.00935"},{"key":"8839_CR26","doi-asserted-by":"crossref","unstructured":"Li F, Tian C, Zuo W et al (2018) Learning spatial-temporal regularized correlation filters for visual tracking[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4904\u20134913","DOI":"10.1109\/CVPR.2018.00515"},{"key":"8839_CR27","unstructured":"Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration [C]. ECCV, 466-1"},{"issue":"8","key":"8839_CR28","doi-asserted-by":"publisher","first-page":"3766","DOI":"10.1109\/TIP.2019.2902784","volume":"28","author":"W Liu","year":"2019","unstructured":"Liu W, Song Y, Chen D et al (2019) Deformable object tracking with gated fusion[J]. IEEE Trans Image Process 28(8):3766\u20133777","journal-title":"IEEE Trans Image Process"},{"key":"8839_CR29","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhang Y, Hu M et al (2017) Fast tracking via spatio-temporal context learning based on multi-color attributes and pca[C]. In: 2017 IEEE International Conference on Information and Automation (ICIA), pp 398\u2013403. IEEE","DOI":"10.1109\/ICInfA.2017.8078941"},{"key":"8839_CR30","unstructured":"Lu X et al (2019) A daptive region proposal with channel regularization for robust object tracking. IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"8839_CR31","doi-asserted-by":"crossref","unstructured":"Lu X, Ma C, Ni B et al (2018) Deep regression tracking with shrinkage loss[C]. In: Proceedings of the european conference on computer vision (ECCV), pp 353\u2013369","DOI":"10.1007\/978-3-030-01264-9_22"},{"key":"8839_CR32","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.neucom.2019.02.021","volume":"349","author":"X Lu","year":"2019","unstructured":"Lu X, Ni B, Ma C et al (2019) Learning transform-aware attentive network for object tracking[J]. Neurocomputing 349:133\u2013144","journal-title":"Neurocomputing"},{"key":"8839_CR33","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Ma C et al (2019) See more, know more: unsupervised video object segmentation with co-attention siamese networks[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3623\u20133632","DOI":"10.1109\/CVPR.2019.00374"},{"key":"8839_CR34","unstructured":"Lukezic A, Vojir T, Cehovin Zajc L, Matas J, Kristan M (2017) Discriminative correlation filter with channel and spatial reliability. In: IEEE conference on computer vision and pattern recognition. 1, 2, 5, 7"},{"key":"8839_CR35","doi-asserted-by":"crossref","unstructured":"Ma C, Yang X, Zhang C, Yang M-H (2015) Long-term correlation tracking. In: CVPR, pp 5388\u20135396, 5","DOI":"10.1109\/CVPR.2015.7299177"},{"key":"8839_CR36","doi-asserted-by":"crossref","unstructured":"Mueller M, Smith N, Ghanem B (2017) Context-aware correlation filter tracking. CVPR","DOI":"10.1109\/CVPR.2017.152"},{"key":"8839_CR37","unstructured":"Poggio T, Cauwenberghs G (2001) Incremental and decremental support vector machine learning. In: Advances in neural information processing systems, NIPS"},{"key":"8839_CR38","doi-asserted-by":"crossref","unstructured":"Possegger H, Mauthner T, Bischof H (2015) . In: Defense of color-based model-free tracking [C]. CVPR","DOI":"10.1109\/CVPR.2015.7298823"},{"key":"8839_CR39","doi-asserted-by":"crossref","unstructured":"Sun SJ, Akhtar N, Song H963S et al (2019) Deep affinity network for multiple object tracking[J]. IEEE transactions on pattern analysis and machine intelligence","DOI":"10.1109\/TPAMI.2019.2929520"},{"key":"8839_CR40","doi-asserted-by":"crossref","unstructured":"Ullah M, Cheikh FA (2018) Deep feature based end-to-end transportation network for multi-target tracking[C]. In: 2018 25th IEEE international conference on image processing (ICIP), pp 3738\u20133742. IEEE","DOI":"10.1109\/ICIP.2018.8451472"},{"key":"8839_CR41","doi-asserted-by":"crossref","unstructured":"Ullah M, Mohammed AK, Cheikh FA et al (2017) A hierarchical feature model for multi-target tracking[C]. In: 2017 IEEE international conference on image processing (ICIP), pp 2612\u20132616. IEEE","DOI":"10.1109\/ICIP.2017.8296755"},{"issue":"7","key":"8839_CR42","first-page":"466\u20131\u2013466","volume":"466-1","author":"M Ullah","year":"2019","unstructured":"Ullah M, Ullah H, Cheikh FA (2019) Single shot appearance model (ssam) for multi-target tracking[J]. Electron Imaging 466-1(7):466\u20131\u2013466-6","journal-title":"Electron Imaging"},{"key":"8839_CR43","doi-asserted-by":"crossref","unstructured":"Van de Weijer J, Schmid C, Verbeek JJ, Larlus D (2009) Learning color names for real-world applications. TIP 18(7):1512\u20131524","DOI":"10.1109\/TIP.2009.2019809"},{"key":"8839_CR44","doi-asserted-by":"crossref","unstructured":"Voigtlaender P, Krause M, Osep A et al (2019) MOTS: multi-object tracking and segmentation[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7942\u20137951","DOI":"10.1109\/CVPR.2019.00813"},{"issue":"23","key":"8839_CR45","doi-asserted-by":"publisher","first-page":"5952","DOI":"10.1109\/TSP.2019.2946023","volume":"67","author":"BN Vo","year":"2019","unstructured":"Vo BN, Vo BT, Beard M (2019) Multi-sensor multi-object tracking with the generalized labeled multi-Bernoulli filter[J]. IEEE Trans Signal Process 67(23):5952\u20135967","journal-title":"IEEE Trans Signal Process"},{"key":"8839_CR46","doi-asserted-by":"crossref","unstructured":"Wang Q, Zhang L, Bertinetto L et al (2019) Fast online object tracking and segmentation: A unifying approach[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1328\u20131338","DOI":"10.1109\/CVPR.2019.00142"},{"key":"8839_CR47","doi-asserted-by":"crossref","unstructured":"Wang T, Piao Y, Li X et al (2019) Deep learning for light field saliency detection[C]","DOI":"10.1109\/ICCV.2019.00893"},{"key":"8839_CR48","doi-asserted-by":"crossref","unstructured":"Wang T, Zhang L, Wang S et al (2018) Detect globally, refine locally: a novel approach to saliency detection[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3127\u20133135","DOI":"10.1109\/CVPR.2018.00330"},{"key":"8839_CR49","doi-asserted-by":"crossref","unstructured":"Wang Z, Xu J, Liu L et al (2019) Ranet: ranking attention network for fast video object segmentation[C]. In: Proceedings of the IEEE international conference on computer vision, pp 3978\u20133987","DOI":"10.1109\/ICCV.2019.00408"},{"key":"8839_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2018.2818138","volume":"2018","author":"J Wu","year":"2018","unstructured":"Wu J, Pan S, Zhu X, et al. (2018) Multi-instance learning with discriminative bag mapping[J]. IEEE Trans Knowl Data Eng 2018:1\u20131","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8839_CR51","doi-asserted-by":"crossref","unstructured":"Wu Y, Lim J, Yang M-H (2013) Online object tracking: A benchmark. In: CVPR, pp 2411\u20132418, 5","DOI":"10.1109\/CVPR.2013.312"},{"issue":"9","key":"8839_CR52","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu Y, Lim J, Yang M-H (2015) Object tracking benchmark. PAMI 37 (9):1834\u20131848, 1, 5, 6","journal-title":"PAMI"},{"issue":"4","key":"8839_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1177352.1177355","volume":"38","author":"A Yilmaz","year":"2006","unstructured":"Yilmaz A, Javed O, Shah M (2006) Object tracking: A survey. ACM Comput Surv 38(4):1\u201345","journal-title":"ACM Comput Surv"},{"key":"8839_CR54","doi-asserted-by":"crossref","unstructured":"Zhang B, Li Z, Cao X, Ye Q, Chen C, Shen L, Perina A, Ji R (2016) Output constraint transfer for kernelized correlation filter in tracking. TSMC","DOI":"10.1109\/TSMC.2016.2629509"},{"key":"8839_CR55","doi-asserted-by":"crossref","unstructured":"Zhang J, Ma S, Sclaroff S (2014) Meem: robust trackingvia multiple experts using entropy minimization. In: ECCV. pp 188\u2013203, 5. Springer, Berlin","DOI":"10.1007\/978-3-319-10599-4_13"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-08839-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-08839-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-08839-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T20:33:23Z","timestamp":1666384403000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-08839-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,20]]},"references-count":55,"journal-issue":{"issue":"29-30","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["8839"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-08839-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,20]]},"assertion":[{"value":"20 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}