{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T17:10:39Z","timestamp":1767892239161,"version":"3.49.0"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T00:00:00Z","timestamp":1708214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T00:00:00Z","timestamp":1708214400000},"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":["62075028"],"award-info":[{"award-number":["62075028"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,5]]},"DOI":"10.1007\/s00521-024-09481-9","type":"journal-article","created":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T19:02:11Z","timestamp":1708282931000},"page":"7639-7656","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Siamada: visual tracking based on Siamese adaptive learning network"],"prefix":"10.1007","volume":"36","author":[{"given":"Xin","family":"Lu","sequence":"first","affiliation":[]},{"given":"Fusheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wanqi","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,18]]},"reference":[{"key":"9481_CR1","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1007\/s11263-022-01732-3","volume":"131","author":"F Wang","year":"2022","unstructured":"Wang F, Cao P, Li F, Wang X, He B, Sun F (2022) Watb: wild animal tracking benchmark. Int J Comput Vis 131:899\u2013917","journal-title":"Int J Comput Vis"},{"key":"9481_CR2","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1109\/JAS.2020.1003453","volume":"8","author":"I Ahmed","year":"2021","unstructured":"Ahmed I, Din S, Jeon G, Piccialli F, Fortino G (2021) Towards collaborative robotics in top view surveillance: a framework for multiple object tracking by detection using deep learning. IEEE\/CAA J Autom Sin 8:1253\u20131270","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"9481_CR3","doi-asserted-by":"crossref","unstructured":"Zhang P, Zhao J, Wang D, Lu H, Ruan X (2022) Visible-thermal UAV tracking: a large-scale benchmark and new baseline. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8876\u20138885","DOI":"10.1109\/CVPR52688.2022.00868"},{"key":"9481_CR4","doi-asserted-by":"crossref","unstructured":"Li B, Wu W, Wang Q, Zhang F, Xing J, Yan J (2018) Siamrpn++: evolution of Siamese visual tracking with very deep networks. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4277\u20134286","DOI":"10.1109\/CVPR.2019.00441"},{"key":"9481_CR5","first-page":"3072","volume":"45","author":"W Hu","year":"2022","unstructured":"Hu W, Wang Q, Zhang L, Bertinetto L, Torr PHS (2022) Siammask: a framework for fast online object tracking and segmentation. IEEE Trans Pattern Anal Mach Intell 45:3072\u20133089","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9481_CR6","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1109\/TCSVT.2021.3072207","volume":"32","author":"T Zhang","year":"2022","unstructured":"Zhang T, Liu X, Zhang Q, Han J (2022) Siamcda: complementarity- and distractor-aware RGB-t tracking based on Siamese network. IEEE Trans Circuits Syst Video Technol 32:1403\u20131417","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"9481_CR7","doi-asserted-by":"crossref","unstructured":"Wang Z, Xie Q, Lai Y, Wu J, Long K, Wang J (2021) Mlvsnet: multi-level voting Siamese network for 3d visual tracking. In: 2021 IEEE\/CVF international conference on computer vision (ICCV), pp 3081\u20133090","DOI":"10.1109\/ICCV48922.2021.00309"},{"issue":"6","key":"9481_CR8","first-page":"1137","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 39(6):1137\u20131149","journal-title":"Adv Neural Inf Process Syst"},{"key":"9481_CR9","unstructured":"Bo L, Yan J, Wei W, Zheng Z, Hu X (2018) High performance visual tracking with Siamese region proposal network. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8971\u20138980"},{"key":"9481_CR10","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: Computer vision\u2014ECCV 2018, pp 103\u2013119","DOI":"10.1007\/978-3-030-01240-3_7"},{"key":"9481_CR11","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2019) Fcos: fully convolutional one-stage object detection. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 9626\u20139635","DOI":"10.1109\/ICCV.2019.00972"},{"key":"9481_CR12","doi-asserted-by":"crossref","unstructured":"Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2019) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9756\u20139765","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"9481_CR13","doi-asserted-by":"publisher","first-page":"3096","DOI":"10.1109\/TPAMI.2021.3050494","volume":"44","author":"X Zhang","year":"2019","unstructured":"Zhang X, Wan F, Liu C, Ji X, Ye Q (2019) Learning to match anchors for visual object detection. IEEE Trans Pattern Anal Mach Intell 44:3096\u20133109","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9481_CR14","doi-asserted-by":"crossref","unstructured":"Kim K-J, Lee HS (2020) Probabilistic anchor assignment with IOU prediction for object detection. In: European conference on computer vision","DOI":"10.1007\/978-3-030-58595-2_22"},{"key":"9481_CR15","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie SJ, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"9481_CR16","first-page":"2386","volume":"44","author":"X Lu","year":"2020","unstructured":"Lu X, Ma C, Shen J, Yang X, Reid ID, Yang M-H (2020) Deep object tracking with shrinkage loss. IEEE Trans Pattern Anal Mach Intell 44:2386\u20132401","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9481_CR17","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1007\/s10044-023-01169-5","volume":"26","author":"H Zhang","year":"2023","unstructured":"Zhang H, Ma Z, Zhang J, Chen F, Song X (2023) Multi-view confidence-aware method for adaptive Siamese tracking with shrink-enhancement loss. Pattern Anal Appl 26:1407\u20131424","journal-title":"Pattern Anal Appl"},{"key":"9481_CR18","doi-asserted-by":"publisher","first-page":"6267","DOI":"10.1109\/TCSVT.2022.3165536","volume":"32","author":"H Zhang","year":"2022","unstructured":"Zhang H, Cheng L, Zhang T, Wang Y, Zhang WJ, Zhang J (2022) Target-distractor aware deep tracking with discriminative enhancement learning loss. IEEE Trans Circuits Syst Video Technol 32:6267\u20136278","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"9481_CR19","doi-asserted-by":"crossref","unstructured":"Fan H, Ling H (2019) Siamese cascaded region proposal networks for real-time visual tracking. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7944\u20137953","DOI":"10.1109\/CVPR.2019.00814"},{"key":"9481_CR20","doi-asserted-by":"crossref","unstructured":"Feng J, Pu S, Zhao K, Zhang H, Du T (2019) Enhanced initialization with multi-stage learning for robust visual tracking. In: 2019 IEEE visual communications and image processing (VCIP), pp 1\u20134","DOI":"10.1109\/VCIP47243.2019.8966006"},{"key":"9481_CR21","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1109\/TCSVT.2020.3006110","volume":"31","author":"N Wang","year":"2020","unstructured":"Wang N, Zhou W-G, Tian Q, Li H (2020) Cascaded regression tracking: towards online hard distractor discrimination. IEEE Trans Circuits Syst Video Technol 31:1580\u20131592","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"9481_CR22","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ins.2022.03.070","volume":"598","author":"K Yang","year":"2022","unstructured":"Yang K, Zhang H, Zhou D, Dong L (2022) Paarpn: probabilistic anchor assignment with region proposal network for visual tracking. Inf Sci 598:19\u201336","journal-title":"Inf Sci"},{"key":"9481_CR23","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.neucom.2021.05.111","volume":"462","author":"L Zhou","year":"2021","unstructured":"Zhou L, He Y, Li W, Mi J-X, Lei BJ (2021) Iou-guided Siamese region proposal network for real-time visual tracking. Neurocomputing 462:544\u2013554","journal-title":"Neurocomputing"},{"key":"9481_CR24","doi-asserted-by":"crossref","unstructured":"Guo D, Wang J, Cui Y, Wang Z, Chen S (2019) Siamcar: Siamese fully convolutional classification and regression for visual tracking. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6268\u20136276","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"9481_CR25","doi-asserted-by":"crossref","unstructured":"Wang Q, Zhang L, Bertinetto L, Hu W, Torr PHS (2018) Fast online object tracking and segmentation: a unifying approach. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1328\u20131338","DOI":"10.1109\/CVPR.2019.00142"},{"key":"9481_CR26","doi-asserted-by":"publisher","first-page":"3597","DOI":"10.1109\/TIP.2021.3060905","volume":"30","author":"W Zhou","year":"2021","unstructured":"Zhou W, Wen L, Zhang L, Du D, Luo T, Wu Y (2021) Siamcan: real-time visual tracking based on Siamese center-aware network. IEEE Trans Image Process 30:3597\u20133609","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"9481_CR27","first-page":"12549","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. Proc AAAI Confer Artif Intell 34(7):12549\u201312556","journal-title":"Proc AAAI Confer Artif Intell"},{"key":"9481_CR28","unstructured":"Kendall A, Gal Y, Cipolla R (2017) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 7482\u20137491"},{"key":"9481_CR29","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS (2016) Fully-convolutional Siamese networks for object tracking. In: Computer science\u2014computer vision and pattern recognition (CVPR)","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"9481_CR30","doi-asserted-by":"crossref","unstructured":"Li P, Chen B, Ouyang W, Wang D, Yang X, Lu H (2019) Gradnet: gradient-guided network for visual object tracking. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 6161\u20136170","DOI":"10.1109\/ICCV.2019.00626"},{"key":"9481_CR31","doi-asserted-by":"crossref","unstructured":"Dong X, Shen J (2018) Triplet loss in Siamese network for object tracking. In: European conference on computer vision","DOI":"10.1007\/978-3-030-01261-8_28"},{"key":"9481_CR32","doi-asserted-by":"crossref","unstructured":"Sosnovik I, Moskalev A, Smeulders AWM (2021) Scale equivariance improves Siamese tracking. In: 2021 IEEE winter conference on applications of computer vision (WACV), pp 2764\u20132773","DOI":"10.1109\/WACV48630.2021.00281"},{"key":"9481_CR33","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neucom.2020.02.080","volume":"401","author":"L Zheng","year":"2020","unstructured":"Zheng L, Chen Y, Tang M, Wang J, Lu H (2020) Siamese deformable cross-correlation network for real-time visual tracking. Neurocomputing 401:36\u201347. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.080","journal-title":"Neurocomputing"},{"issue":"10","key":"9481_CR34","doi-asserted-by":"publisher","first-page":"8173","DOI":"10.1007\/s00521-022-06911-4","volume":"34","author":"H Huang","year":"2022","unstructured":"Huang H, Liu G, Zhang Y, Xiong R, Zhang S (2022) Ensemble Siamese networks for object tracking. Neural Comput Appl 34(10):8173\u20138191. https:\/\/doi.org\/10.1007\/s00521-022-06911-4","journal-title":"Neural Comput Appl"},{"issue":"2","key":"9481_CR35","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1109\/TCSVT.2019.2892759","volume":"30","author":"D Li","year":"2020","unstructured":"Li D, Porikli F, Wen G, Kuai Y (2020) When correlation filters meet Siamese networks for real-time complementary tracking. IEEE Trans Circuits Syst Video Technol 30(2):509\u2013519. https:\/\/doi.org\/10.1109\/TCSVT.2019.2892759","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"9481_CR36","doi-asserted-by":"publisher","unstructured":"Zhong P, Wu W, Dai X, Zhao Q, Li S (2023) Fisher pruning for developing real-time UAV trackers. J Real-Time Image Process. https:\/\/doi.org\/10.1007\/s11554-023-01348-x","DOI":"10.1007\/s11554-023-01348-x"},{"key":"9481_CR37","doi-asserted-by":"crossref","unstructured":"Yan B, Zhao H, Wang D, Lu H, Yang X (2019) \u2019Skimming-perusal\u2019 tracking: a framework for real-time and robust long-term tracking. In: 2019 IEEE\/CVF international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2019.00247"},{"key":"9481_CR38","doi-asserted-by":"crossref","unstructured":"Zhang L, Gonzalez-Garcia A, van\u00a0de Weijer J, Danelljan M, Khan FS (2019) Learning the model update for Siamese trackers. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 4009\u20134018","DOI":"10.1109\/ICCV.2019.00411"},{"key":"9481_CR39","doi-asserted-by":"crossref","unstructured":"Zhang Z, Peng H (2020) Deeper and wider Siamese networks for real-time visual tracking. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00472"},{"key":"9481_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CPVR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"9481_CR41","doi-asserted-by":"crossref","unstructured":"Zheng G-Z, Fu C, Ye J, Li B, Lu G, Pan J-Y (2022) Siamese object tracking for vision-based UAM approaching with pairwise scale-channel attention. In: 2022 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 10486\u201310492","DOI":"10.1109\/IROS47612.2022.9982189"},{"key":"9481_CR42","doi-asserted-by":"publisher","first-page":"9349","DOI":"10.1109\/TII.2022.3228197","volume":"19","author":"G-Z Zheng","year":"2023","unstructured":"Zheng G-Z, Fu C, Ye J, Li B, Lu G, Pan J-Y (2023) Scale-aware Siamese object tracking for vision-based UAM approaching. IEEE Trans Ind Inf 19:9349\u20139360","journal-title":"IEEE Trans Ind Inf"},{"key":"9481_CR43","doi-asserted-by":"crossref","unstructured":"Cao Z, Fu C, Ye J, Li B, Li Y (2021) Siamapn++: Siamese attentional aggregation network for real-time UAV tracking. In: 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 3086\u20133092","DOI":"10.1109\/IROS51168.2021.9636309"},{"key":"9481_CR44","doi-asserted-by":"crossref","unstructured":"Guo D, Shao Y, Cui Y, Wang Z, Zhang L, Shen C (2021) Graph attention tracking. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9538\u20139547","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"9481_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103911","volume":"97","author":"S Wu","year":"2019","unstructured":"Wu S, Li X, Wang X (2019) Iou-aware single-stage object detector for accurate localization. Image Vis Comput 97:103911","journal-title":"Image Vis Comput"},{"key":"9481_CR46","doi-asserted-by":"crossref","unstructured":"Jiang B, Luo R, Mao J, Xiao T, Jiang Y (2018) Acquisition of localization confidence for accurate object detection. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) 2018 European Conference on Computer Vision (ECCV), pp 784\u2013799","DOI":"10.1007\/978-3-030-01264-9_48"},{"issue":"4","key":"9481_CR47","doi-asserted-by":"crossref","first-page":"5158","DOI":"10.1109\/TPAMI.2022.3200725","volume":"45","author":"Z Chen","year":"2023","unstructured":"Chen Z, Zhong B, Li G, Zhang S, Ji R, Tang Z, Li X (2023) Siamban: target-aware tracking with Siamese box adaptive network. IEEE Trans Pattern Anal Mach Intell 45(4):5158\u20135173","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9481_CR48","doi-asserted-by":"crossref","unstructured":"Peng J, Jiang Z, Gu Y, Wu Y, Wang Y, Tai Y, Wang C, Lin W (2021) Siamrcr: reciprocal classification and regression for visual object tracking, pp. 952\u2013958. arXiv:2105.11237. https:\/\/api.semanticscholar.org\/CorpusID:235166830","DOI":"10.24963\/ijcai.2021\/132"},{"key":"9481_CR49","doi-asserted-by":"publisher","first-page":"8785","DOI":"10.1109\/TIP.2021.3120305","volume":"30","author":"F Tang","year":"2021","unstructured":"Tang F, Ling Q (2021) Learning to rank proposals for Siamese visual tracking. IEEE Trans Image Process 30:8785\u20138796","journal-title":"IEEE Trans Image Process"},{"key":"9481_CR50","doi-asserted-by":"crossref","unstructured":"Nie J, Wu H, He Z, Yang Y, Gao M, Dong Z (2022) Learning localization-aware target confidence for Siamese visual tracking. arXiv:2204.14093","DOI":"10.1109\/TMM.2022.3206668"},{"key":"9481_CR51","doi-asserted-by":"crossref","unstructured":"Fan H, Ling H (2021) Cract: cascaded regression-align-classification for robust tracking. In: 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 7013\u20137020","DOI":"10.1109\/IROS51168.2021.9636803"},{"key":"9481_CR52","doi-asserted-by":"crossref","unstructured":"Zhang Z, Peng H (2020) Ocean: object-aware anchor-free tracking. In: European conference on computer vision, pp 771\u2013787","DOI":"10.1007\/978-3-030-58589-1_46"},{"key":"9481_CR53","doi-asserted-by":"publisher","first-page":"9614","DOI":"10.1109\/TIP.2020.3029897","volume":"29","author":"Y Zheng","year":"2020","unstructured":"Zheng Y, Liu X, Cheng X, Zhang K, Wu Y, Chen S (2020) Multi-task deep dual correlation filters for visual tracking. IEEE Trans Image Process 29:9614\u20139626","journal-title":"IEEE Trans Image Process"},{"key":"9481_CR54","doi-asserted-by":"publisher","first-page":"8204","DOI":"10.1109\/TCSVT.2021.3071128","volume":"32","author":"Y Zheng","year":"2022","unstructured":"Zheng Y, Liu X, Xiao B, Cheng X, Wu Y, Chen S (2022) Multi-task convolution operators with object detection for visual tracking. IEEE Trans Circuits Syst Video Technol 32:8204\u20138216","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"9481_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101816","volume":"97","author":"Y Cai","year":"2023","unstructured":"Cai Y, Sui X, Gu G (2023) Multi-modal multi-task feature fusion for RGBT tracking. Inf Fus 97:101816","journal-title":"Inf Fus"},{"key":"9481_CR56","doi-asserted-by":"publisher","first-page":"7933","DOI":"10.1007\/s11063-023-11290-5","volume":"55","author":"F Wang","year":"2023","unstructured":"Wang F, Cao P, Wang X, He B, Sun F (2023) SiamADT: Siamese attention and deformable features fusion network for visual object tracking. Neural Proc Lett 55:7933\u20137950","journal-title":"Neural Proc Lett"},{"key":"9481_CR57","unstructured":"Marvasti-Zadeh SM, Khaghani J, Ghanei-Yakhdan H, Kasaei S, Cheng L (2020) Comet: context-aware IOU-guided network for small object tracking. In: Asian conference on computer vision. https:\/\/api.semanticscholar.org\/CorpusID:219305183"},{"key":"9481_CR58","doi-asserted-by":"crossref","unstructured":"Tang F, Ling Q (2022) Ranking-based Siamese visual tracking. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 8731\u20138740","DOI":"10.1109\/CVPR52688.2022.00854"},{"key":"9481_CR59","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.1093\/comjnl\/bxab026","volume":"65","author":"Y Wang","year":"2021","unstructured":"Wang Y, Wang F, Wang C, Sun F, He J (2021) Learning saliency-aware correlation filters for visual tracking. Comput J 65:1846\u20131859","journal-title":"Comput J"},{"key":"9481_CR60","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1007\/s00530-022-01043-0","volume":"29","author":"F Sun","year":"2022","unstructured":"Sun F, Zhao T, Zhu B, Jia X, Wang F (2022) Deblurring transformer tracking with conditional cross-attention. Multimedia Syst 29:1131\u20131144","journal-title":"Multimedia Syst"},{"key":"9481_CR61","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhong B, Li G, Zhang S, Ji R (2020) Siamese box adaptive network for visual tracking. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6667\u20136676","DOI":"10.1109\/CVPR42600.2020.00670"},{"issue":"1","key":"9481_CR62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em\u2014algorithm plus discussions on the paper. J R Stat Soc. Ser B (Methodol) 39(1):1\u201338","journal-title":"J R Stat Soc. Ser B (Methodol)"},{"key":"9481_CR63","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2014","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein MS, Berg AC, Fei-Fei L (2014) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252","journal-title":"Int J Comput Vis"},{"key":"9481_CR64","doi-asserted-by":"crossref","unstructured":"Wu Y, Lim J, Yang M-H (2013) Online object tracking: a benchmark. In: 2013 IEEE conference on computer vision and pattern recognition, pp 2411\u20132418","DOI":"10.1109\/CVPR.2013.312"},{"key":"9481_CR65","unstructured":"et al MK (2018) The sixth visual object tracking vot2018 challenge results. In: ECCV workshops"},{"key":"9481_CR66","doi-asserted-by":"crossref","unstructured":"Li S, Yeung DY (2017) Visual object tracking for unmanned aerial vehicles: a benchmark and new motion models. In: AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11205"},{"key":"9481_CR67","doi-asserted-by":"crossref","unstructured":"M.\u00a0Mueller, N.S., Ghanem, B (2016) A benchmark and simulator for UAV tracking. In: European conference on computer vision (ECCV), pp 445\u2013461","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"9481_CR68","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TPAMI.2019.2957464","volume":"43","author":"L Huang","year":"2018","unstructured":"Huang L, Zhao X, Huang K (2018) Got-10k: a large high-diversity benchmark for generic object tracking in the wild. IEEE Trans Pattern Anal Mach Intell 43:1562\u20131577","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9481_CR69","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 (2018) Lasot: a high-quality benchmark for large-scale single object tracking. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5369\u20135378","DOI":"10.1109\/CVPR.2019.00552"},{"key":"9481_CR70","doi-asserted-by":"crossref","unstructured":"Voigtlaender P, Luiten J, Torr PHS, Leibe B (2020) Siam r-CNN: visual tracking by re-detection. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6577\u20136587","DOI":"10.1109\/CVPR42600.2020.00661"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09481-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09481-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09481-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T19:43:40Z","timestamp":1731354220000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09481-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,18]]},"references-count":70,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9481"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09481-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,18]]},"assertion":[{"value":"9 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The data used in this study are public datasets published on official websites and do not involve human participants and\/or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}