{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:49Z","timestamp":1740108049937,"version":"3.37.3"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T00:00:00Z","timestamp":1635292800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T00:00:00Z","timestamp":1635292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972097","61672159"],"award-info":[{"award-number":["61972097","61672159"]}],"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":["U1705262","61672158"],"award-info":[{"award-number":["U1705262","61672158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005270","name":"Fujian Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["2017H0015"],"award-info":[{"award-number":["2017H0015"]}],"id":[{"id":"10.13039\/501100005270","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2018J1798","2018J07005"],"award-info":[{"award-number":["2018J1798","2018J07005"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s00521-021-06638-8","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T18:02:35Z","timestamp":1635357755000},"page":"3745-3765","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning deep convolutional descriptor aggregation for efficient visual tracking"],"prefix":"10.1007","volume":"34","author":[{"given":"Xiao","family":"Ke","sequence":"first","affiliation":[]},{"given":"Yuezhou","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6586-4588","authenticated-orcid":false,"given":"Wenzhong","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yanyan","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Bau D, Zhou B, Khosla A, Oliva A, Torralba A(2017) Network dissection: quantifying interpretability of deep visual representations. In: Proceedings of the CVPR, pp 6541\u20136549","key":"6638_CR1","DOI":"10.1109\/CVPR.2017.354"},{"unstructured":"Bertinetto L, Henriques J, Valmadre J, Torr P, Vedaldi A (2016) Learning feed-forward one-shot learners. In: Proceeding of the NIPS, pp 523\u2013531","key":"6638_CR2"},{"doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PHS (2016) Staple: complementary learners for real-time tracking. In: Proceeding of the CVPR, pp 1401\u20131409","key":"6638_CR3","DOI":"10.1109\/CVPR.2016.156"},{"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 ECCVW, pp 850\u2013865. Springer","key":"6638_CR4","DOI":"10.1007\/978-3-319-48881-3_56"},{"issue":"4","key":"6638_CR5","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1109\/TIP.2017.2669880","volume":"26","author":"C Zhizhen","year":"2017","unstructured":"Zhizhen C, Hongyang L, Huchuan L, Ming-Hsuan Y (2017) Dual deep network for visual tracking. IEEE Trans Image Process 26(4):2005\u20132015","journal-title":"IEEE Trans Image Process"},{"doi-asserted-by":"crossref","unstructured":"Choi J, Jin\u00a0CH, Fischer T, Yun S, Lee K, Jeong J, Demiris Y, Young\u00a0CJ (2018) Context-aware deep feature compression for high-speed visual tracking. In: Proceeding of the CVPR, pp 479\u2013488","key":"6638_CR6","DOI":"10.1109\/CVPR.2018.00057"},{"doi-asserted-by":"crossref","unstructured":"Chu P, Ling H (2019) Famnet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In: Proceeding of the CVPR, pp 6172\u20136181","key":"6638_CR7","DOI":"10.1109\/ICCV.2019.00627"},{"unstructured":"Danelljan M (2018) Learning convolution operators for visual tracking, vol 1926. Link\u00f6ping University Electronic Press, Link\u00f6ping","key":"6638_CR8"},{"doi-asserted-by":"crossref","unstructured":"Danelljan M, H\u00e4ger G, Khan FS, Felsberg M (2015) Coloring channel representations for visual tracking. In: Scandinavian conference on image analysis, pp 117\u2013129. Springer","key":"6638_CR9","DOI":"10.1007\/978-3-319-19665-7_10"},{"doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Shahbaz\u00a0KF, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. In: Proceeding of the ICCVW, pp 58\u201366","key":"6638_CR10","DOI":"10.1109\/ICCVW.2015.84"},{"doi-asserted-by":"crossref","unstructured":"Danelljan M, Hager G, Shahbaz\u00a0KF, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceeding of the ICCV, pp 4310\u20134318","key":"6638_CR11","DOI":"10.1109\/ICCV.2015.490"},{"issue":"8","key":"6638_CR12","first-page":"1561","volume":"39","author":"D Martin","year":"2016","unstructured":"Martin D, Gustav H, Shahbaz KF, Michael F (2016a) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561\u20131575","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"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: Proceeding of the ECCV, pp. 472\u2013488. Springer","key":"6638_CR13","DOI":"10.1007\/978-3-319-46454-1_29"},{"doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Shahbaz KF, Felsberg M (2017) Eco: efficient convolution operators for tracking. In: Proceeding of the CVPR, pp 6638\u20136646","key":"6638_CR14","DOI":"10.1109\/CVPR.2017.733"},{"doi-asserted-by":"crossref","unstructured":"Dong X, Shen J (2018) Triplet loss in siamese network for object tracking. In: Proceeding of the ECCV, pp 459\u2013474","key":"6638_CR15","DOI":"10.1007\/978-3-030-01261-8_28"},{"doi-asserted-by":"crossref","unstructured":"Fan H, Lin L, Yang F, Chu P, Deng G, Yu S, Bai H, Xu Y, Liao Y, Ling Y (2019) Lasot: a high-quality benchmark for large-scale single object tracking. In: Proceeding of the CVPR, pp 5374\u20135383","key":"6638_CR16","DOI":"10.1109\/CVPR.2019.00552"},{"unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the ICML, pp 1126\u20131135. JMLR. org","key":"6638_CR17"},{"doi-asserted-by":"crossref","unstructured":"Gao J, Zhang T, Xu C (2019) Graph convolutional tracking. In: Proceedings of the CVPR, pp 4649\u20134659","key":"6638_CR18","DOI":"10.1109\/CVPR.2019.00478"},{"doi-asserted-by":"crossref","unstructured":"He A, Luo C, Tian X, Zeng W (2018) A twofold siamese network for real-time object tracking. In: Proceedings of the CVPR, pp 4834\u20134843","key":"6638_CR19","DOI":"10.1109\/CVPR.2018.00508"},{"doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the CVPR, pp 770\u2013778","key":"6638_CR20","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"He Z, Fan Y, Zhuang J, Dong Y, Bai HL (2017) Correlation filters with weighted convolution responses. In: Proceedings of the ICCVW, pp 1992\u20132000","key":"6638_CR21","DOI":"10.1109\/ICCVW.2017.233"},{"doi-asserted-by":"crossref","unstructured":"Held D, Thrun S, Sav S (2016) Learning to track at 100 fps with deep regression networks. In: Proceedings of the ECCV, pp 749\u2013765. Springer","key":"6638_CR22","DOI":"10.1007\/978-3-319-46448-0_45"},{"issue":"3","key":"6638_CR23","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"F Henriques Jo\u00e3o","year":"2014","unstructured":"Henriques Jo\u00e3o F, Rui C, Pedro M, Jorge B (2014) 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"},{"unstructured":"Kiani\u00a0GH, Sim T, Lucey S (2015) Correlation filters with limited boundaries. In: Proceedings of the CVPR, pp 4630\u20134638","key":"6638_CR24"},{"unstructured":"Kiani\u00a0GH, Fagg A, Huang C, Ramanan D, Lucey S (2017) Need for speed: a benchmark for higher frame rate object tracking. In: Proceedings of the ICCV, pp 1125\u20131134","key":"6638_CR25"},{"unstructured":"Kiani\u00a0GH, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of the ICCV, pp 1135\u20131143","key":"6638_CR26"},{"unstructured":"Kristan M, Lukezic A, Danelljan M, \u010cehovin\u00a0ZL, Matas J (2020) The new vot2020 short-term tracking performance evaluation protocol and measures","key":"6638_CR27"},{"doi-asserted-by":"crossref","unstructured":"Kristan M, Matas J, Leonardis A, Felsberg M, Cehovin L, Fern\u00e1ndez G, Vojir H, Tomas et\u00a0al (2016) The visual object tracking vot2016 challenge results. In: Proceedings of the ECCVW, vol 2, p 8","key":"6638_CR28","DOI":"10.1007\/978-3-319-48881-3_54"},{"issue":"11","key":"6638_CR29","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1109\/TPAMI.2016.2516982","volume":"38","author":"M Kristan","year":"2016","unstructured":"Kristan M, Matas J, Leonardis A, Vojir T, Pflugfelder R, Fernandez G, Nebehay G, Porikli F, \u010cehovin L (2016) A novel performance evaluation methodology for single-target trackers. IEEE Trans Pattern Anal Mach Intell 38(11):2137\u20132155. https:\/\/doi.org\/10.1109\/TPAMI.2016.2516982","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"doi-asserted-by":"crossref","unstructured":"Kristan M, Leonardis A, Matas A, Felsberg M, Pflugfelder R, Cehovin\u00a0ZL, Vojir L, Hager G, Lukezic A, Eldesokey A et\u00a0al (2017) The visual object tracking vot2017 challenge results. In: Proceedings of the ICCVW, pp 1949\u20131972","key":"6638_CR30","DOI":"10.1109\/ICCVW.2017.230"},{"unstructured":"Matej K, Jiri M, Ales L, Michael F, Roman P, Joni-Kristian K, Luka CZ, Ondrej D, Alan L, Amanda B et al (2019) The seventh visual object tracking vot2019 challenge results. In: Proceedings of the ICCVW","key":"6638_CR31"},{"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 CVPR, pp 8971\u20138980","key":"6638_CR32","DOI":"10.1109\/CVPR.2018.00935"},{"doi-asserted-by":"crossref","unstructured":"Li B, Wu W, Wang Q, Zhang F, Xing F, Yan J (2019) Siamrpn++: evolution of siamese visual tracking with very deep networks. In: Proceedings of the CVPR, pp 4282\u20134291","key":"6638_CR33","DOI":"10.1109\/CVPR.2019.00441"},{"doi-asserted-by":"crossref","unstructured":"Li P, Chen B, Ouyang W, Wang D, Yang X, Lu X (2019) Gradnet: gradient-guided network for visual object tracking. In: Proceedings of the ICCV, pp 6162\u20136171","key":"6638_CR34","DOI":"10.1109\/ICCV.2019.00626"},{"doi-asserted-by":"crossref","unstructured":"Li X, Ma C, Wu B, He Z, Yang MH (2019) Target-aware deep tracking. In: Proceedings of the CVPR, pp 1369\u20131378","key":"6638_CR35","DOI":"10.1109\/CVPR.2019.00146"},{"doi-asserted-by":"crossref","unstructured":"Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: Proceedings of the ECCV, pp 254\u2013265. Springer","key":"6638_CR36","DOI":"10.1007\/978-3-319-16181-5_18"},{"doi-asserted-by":"crossref","unstructured":"Yang L, Jianke Z, Hoi Steven CH, Wenjie S, Zhefeng W, Hantang L (2019) Robust estimation of similarity transformation for visual object tracking. In: Proc AAAI 33:8666\u20138673","key":"6638_CR37","DOI":"10.1609\/aaai.v33i01.33018666"},{"unstructured":"Shuai L, Shuai W, Xinyu L, Chin-Teng L, Zhihan L (2020) Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE Trans Fuzzy Syst","key":"6638_CR38"},{"key":"6638_CR39","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1007\/s00521-020-05021-3","volume":"33","author":"L Shuai","year":"2021","unstructured":"Shuai L, Xinyu L, Shuai W, Khan M (2021) Fuzzy-aided solution for out-of-view challenge in visual tracking under iot-assisted complex environment. Neural Comput Appl 33:1055\u20131065","journal-title":"Neural Comput Appl"},{"unstructured":"Shuai L, Shuai W, Xinyu L, Gandomi Amir H, Mahmoud D, Khan M, de Albuquerque Victor Hugo C, (2021) Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans Multimedia","key":"6638_CR40"},{"issue":"8","key":"6638_CR41","doi-asserted-by":"publisher","first-page":"3766","DOI":"10.1109\/TIP.2019.2902784","volume":"28","author":"L Wenxi","year":"2019","unstructured":"Wenxi L, Yibing S, Dengsheng C, He Shengfeng Yu, Yuanlong YT, Hancke Gehard P, Lau Rynson WH (2019) Deformable object tracking with gated fusion. IEEE Trans Image Process 28(8):3766\u20133777","journal-title":"IEEE Trans Image Process"},{"doi-asserted-by":"crossref","unstructured":"Ma C, Yang X, Zhang C, Yang MH (2015) Long-term correlation tracking. In: Proceedings of the CVPR, pp 5388\u20135396","key":"6638_CR42","DOI":"10.1109\/CVPR.2015.7299177"},{"issue":"11","key":"6638_CR43","first-page":"2709","volume":"41","author":"M Chao","year":"2018","unstructured":"Chao M, Jia-Bin H, Xiaokang Y, Ming-Hsuan Y (2018) Robust visual tracking via hierarchical convolutional features. IEEE Trans Pattern Anal Mach Intell 41(11):2709\u20132723","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"doi-asserted-by":"crossref","unstructured":"Marvasti-Zadeh MH, Ghanei-Yakhdan H, Kasaei S (2021) Efficient scale estimation methods using lightweight deep convolutional neural networks for visual tracking. Neural Comput Appl, pp 1\u201316","key":"6638_CR44","DOI":"10.1007\/s00521-020-05586-z"},{"unstructured":"Munkhdalai T, Yu H (2017) Meta networks. In: Proceedings of the ICML, pp 2554\u20132563. JMLR. org","key":"6638_CR45"},{"doi-asserted-by":"crossref","unstructured":"Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the CVPR, pp 4293\u20134302","key":"6638_CR46","DOI":"10.1109\/CVPR.2016.465"},{"key":"6638_CR47","first-page":"1","volume":"16","author":"P Zaiyu","year":"2020","unstructured":"Zaiyu P, Jun W, Guoqing W, Jihong Z (2020) Multi-scale deep representation aggregation for vein recognition. IEEE Trans Inf Forens Security 16:1\u201315","journal-title":"IEEE Trans Inf Forens Security"},{"unstructured":"Adam P, Sam G, Francisco M, Adam L, James B, Gregory C, Trevor K, Zeming L, Natalia G, Luca A et al (2019) Pytorch: An imperative style, high-performance deep learning library. In: Proceedings of the NIPS 8024\u20138035","key":"6638_CR48"},{"issue":"5","key":"6638_CR49","first-page":"1116","volume":"41","author":"Q Yuankai","year":"2018","unstructured":"Yuankai Q, Shengping Z, Lei Q, Qingming H, Hongxun Y, Jongwoo L, Ming-Hsuan Y (2018) Hedging deep features for visual tracking. IEEE Trans Pattern Anal Mach Intell 41(5):1116\u20131130","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"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 CVPR, pp 5296\u20135305","key":"6638_CR50","DOI":"10.1109\/CVPR.2017.789"},{"issue":"3","key":"6638_CR51","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"R Olga","year":"2015","unstructured":"Olga R, Jia D, Hao S, Jonathan K, Sanjeev S, Sean M, Zhiheng H, Andrej K, Aditya K, Michael B, Berg Alexander C, Li F-F (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis (IJCV) 115(3):211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis (IJCV)"},{"unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","key":"6638_CR52"},{"doi-asserted-by":"crossref","unstructured":"Sun C, Wang D, Lu H, Yang M-H (2018) Learning spatial-aware regressions for visual tracking. In: Proceedings of the CVPR, pp 8962\u20138970","key":"6638_CR53","DOI":"10.1109\/CVPR.2018.00934"},{"doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed P, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the CVPR, pp 1\u20139","key":"6638_CR54","DOI":"10.1109\/CVPR.2015.7298594"},{"doi-asserted-by":"crossref","unstructured":"Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PHS (2017) End-to-end representation learning for correlation filter based tracking. In: Proceedings of the CVPR, pp 2805\u20132813","key":"6638_CR55","DOI":"10.1109\/CVPR.2017.531"},{"doi-asserted-by":"crossref","unstructured":"Wang G, Luo C, Xiong Z, Zeng Z (2019) Spm-tracker: series-parallel matching for real-time visual object tracking. In: Proceedings of the CVPR, pp 3643\u20133652","key":"6638_CR56","DOI":"10.1109\/CVPR.2019.00376"},{"doi-asserted-by":"crossref","unstructured":"Wang L, Ouyang W, Wang X, Lu H (2015) Visual tracking with fully convolutional networks. In: Proceedings of the ICCV, pp 3119\u20133127","key":"6638_CR57","DOI":"10.1109\/ICCV.2015.357"},{"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 CVPR, pp 1308\u20131317","key":"6638_CR58","DOI":"10.1109\/CVPR.2019.00140"},{"issue":"6","key":"6638_CR59","doi-asserted-by":"publisher","first-page":"2868","DOI":"10.1109\/TIP.2017.2688133","volume":"26","author":"W Xiu-Shen","year":"2017","unstructured":"Xiu-Shen W, Jian-Hao L, Jianxin W, Zhi-Hua Z (2017) Selective convolutional descriptor aggregation for fine-grained image retrieval. IEEE Trans Image Process 26(6):2868\u20132881","journal-title":"IEEE Trans Image Process"},{"key":"6638_CR60","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.patcog.2018.10.022","volume":"88","author":"W Xiu-Shen","year":"2019","unstructured":"Xiu-Shen W, Chen-Lin Z, Jianxin W, Chunhua S, Zhi-Hua Z (2019) Unsupervised object discovery and co-localization by deep descriptor transformation. Pattern Recogn 88:113\u2013126","journal-title":"Pattern Recogn"},{"doi-asserted-by":"crossref","unstructured":"Wu Y, Lim J, Yang M-H (2013) Online object tracking: a benchmark. In: Proceedings of the CVPR, pp 2411\u20132418","key":"6638_CR61","DOI":"10.1109\/CVPR.2013.312"},{"issue":"9","key":"6638_CR62","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"W Yi","year":"2015","unstructured":"Yi W, Jongwoo L, Ming-Hsuan Y (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834\u20131848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"doi-asserted-by":"crossref","unstructured":"Xu J, Shi C, Qi C, Wang C, Xiao B (2018) Unsupervised part-based weighting aggregation of deep convolutional features for image retrieval. In: Proceedings of the AAAI, vol 32","key":"6638_CR63","DOI":"10.1609\/aaai.v32i1.12231"},{"issue":"18","key":"6638_CR64","doi-asserted-by":"publisher","first-page":"14335","DOI":"10.1007\/s00521-019-04238-1","volume":"32","author":"Y Kang","year":"2020","unstructured":"Kang Y, Huihui S, Kaihua Z, Qingshan L (2020) Hierarchical attentive siamese network for real-time visual tracking. Neural Comput Appl 32(18):14335\u201314346","journal-title":"Neural Comput Appl"},{"doi-asserted-by":"crossref","unstructured":"Yang T, Chan AB (2018) Learning dynamic memory networks for object tracking. In: Proceedings of the ECCV, pp 152\u2013167","key":"6638_CR65","DOI":"10.1007\/978-3-030-01240-3_10"},{"unstructured":"Tianyu Y, Chan Antoni B (2019) Visual tracking via dynamic memory networks. IEEE Trans Pattern Anal Mach Intell","key":"6638_CR66"},{"doi-asserted-by":"crossref","unstructured":"Yang Y, De-Chuan Z, Ying F, Yuan J, Zhi-Hua Z (2017) Deep learning for fixed model reuse. In: Proceedings of the AAAI","key":"6638_CR67","DOI":"10.1609\/aaai.v31i1.10855"},{"doi-asserted-by":"crossref","unstructured":"Yin J, Wang W, Meng Q, Yang R, Shen J (2020) A unified object motion and affinity model for online multi-object tracking. In: Proceedings of the CVPR, pp 6768\u20136777","key":"6638_CR68","DOI":"10.1109\/CVPR42600.2020.00680"},{"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 ECCV, pp 188\u2013203. Springer","key":"6638_CR69","DOI":"10.1007\/978-3-319-10599-4_13"},{"doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the CVPR, pp 2921\u20132929","key":"6638_CR70","DOI":"10.1109\/CVPR.2016.319"},{"doi-asserted-by":"crossref","unstructured":"Zhu J, Yang H, Liu N, Kim M, Zhang W, Yang MH (2018) Online multi-object tracking with dual matching attention networks. In: Proceedings of the ECCV, pp 366\u2013382","key":"6638_CR71","DOI":"10.1007\/978-3-030-01228-1_23"},{"issue":"7","key":"6638_CR72","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1007\/s13042-018-0898-2","volume":"10","author":"Z Jie","year":"2019","unstructured":"Jie Z, Shufang W, Hong Z, Yan L, Li Z (2019) Multi-center convolutional descriptor aggregation for image retrieval. Int J Mach Learn Cybern 10(7):1863\u20131873","journal-title":"Int J Mach Learn Cybern"},{"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 ECCV, pp 101\u2013117","key":"6638_CR73","DOI":"10.1007\/978-3-030-01240-3_7"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06638-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06638-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06638-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T23:37:06Z","timestamp":1673653026000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06638-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,27]]},"references-count":73,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["6638"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06638-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2021,10,27]]},"assertion":[{"value":"15 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}