{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T02:29:20Z","timestamp":1750127360299},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00521-023-08422-2","type":"journal-article","created":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T06:02:34Z","timestamp":1677909754000},"page":"12611-12625","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["RHL-track: visual object tracking based on recurrent historical localization"],"prefix":"10.1007","volume":"35","author":[{"given":"Feiyu","family":"Meng","sequence":"first","affiliation":[]},{"given":"Xiaomei","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"issue":"9","key":"8422_CR1","doi-asserted-by":"publisher","first-page":"4354","DOI":"10.1109\/TIP.2016.2590322","volume":"25","author":"S Yi","year":"2016","unstructured":"Yi S, Li H, Wang X (2016) Pedestrian behavior modeling from stationary crowds with applications to intelligent surveillance. IEEE Trans Image Process 25(9):4354\u20134368. https:\/\/doi.org\/10.1109\/TIP.2016.2590322","journal-title":"IEEE Trans Image Process"},{"key":"8422_CR2","doi-asserted-by":"publisher","unstructured":"Chen X, Ma H, Wan J, Li B, Xia T (2017) Multi-view 3D object detection network for autonomous driving. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6526\u20136534 . https:\/\/doi.org\/10.1109\/CVPR.2017.691","DOI":"10.1109\/CVPR.2017.691"},{"key":"8422_CR3","doi-asserted-by":"crossref","unstructured":"Yuan D, Shu X, Liu Q, Zhang X, He Z (2022) Robust thermal infrared tracking via an adaptively multi-feature fusion model. Neural Comput Appl 1\u201312","DOI":"10.1007\/s00521-022-07867-1"},{"issue":"7","key":"8422_CR4","doi-asserted-by":"publisher","first-page":"5757","DOI":"10.1007\/s00521-021-06704-1","volume":"34","author":"Y Wang","year":"2022","unstructured":"Wang Y, Wei X, Tang X, Wu J, Fang J (2022) Response map evaluation for RGBT tracking. Neural Comput Appl 34(7):5757\u20135769","journal-title":"Neural Comput Appl"},{"key":"8422_CR5","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"8422_CR6","doi-asserted-by":"crossref","unstructured":"Kiran M, Nguyen-Meidine LT, Sahay R, Cruz RMOE, Blais-Morin L-A, Granger E (2022) Dynamic template selection through change detection for adaptive Siamese tracking. arXiv preprint arXiv:2203.03181","DOI":"10.1109\/IJCNN55064.2022.9892497"},{"key":"8422_CR7","doi-asserted-by":"publisher","unstructured":"Fan H, Lin L, Yang F, Chu P, Deng G, Yu S, Bai H, Xu Y, Liao C, Ling H (2019) 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. https:\/\/doi.org\/10.1109\/CVPR.2019.00552","DOI":"10.1109\/CVPR.2019.00552"},{"key":"8422_CR8","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1007\/978-3-319-48881-3_56","volume-title":"Computer Vision\u2014ECCV 2016 Workshops","author":"L Bertinetto","year":"2016","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS (2016) Fully-convolutional Siamese networks for object tracking. In: Hua G, J\u00e9gou H (eds) Computer Vision\u2014ECCV 2016 Workshops. Springer, Cham, pp 850\u2013865"},{"key":"8422_CR9","doi-asserted-by":"publisher","unstructured":"Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with Siamese region proposal network. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 8971\u20138980. https:\/\/doi.org\/10.1109\/CVPR.2018.00935","DOI":"10.1109\/CVPR.2018.00935"},{"key":"8422_CR10","doi-asserted-by":"publisher","unstructured":"Li B, Wu W, Wang Q, Zhang F, Xing J, Yan J (2019) 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. https:\/\/doi.org\/10.1109\/CVPR.2019.00441","DOI":"10.1109\/CVPR.2019.00441"},{"key":"8422_CR11","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/978-3-030-01240-3_7","volume-title":"Computer Vision\u2014ECCV 2018","author":"Z Zhu","year":"2018","unstructured":"Zhu Z, Wang Q, Li B, Wu W, Yan J, Hu W (2018) Distractor-aware Siamese networks for visual object tracking. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision\u2014ECCV 2018. Springer, Cham, pp 103\u2013119"},{"key":"8422_CR12","doi-asserted-by":"publisher","unstructured":"Guo, D., Wang J, Cui Y, Wang Z, Chen S (2020) 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. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00630","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"8422_CR13","doi-asserted-by":"publisher","unstructured":"Danelljan M, Bhat G, Khan FS, Felsberg M (2019) Atom: accurate tracking by overlap maximization. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4655\u20134664. https:\/\/doi.org\/10.1109\/CVPR.2019.00479","DOI":"10.1109\/CVPR.2019.00479"},{"issue":"7","key":"8422_CR14","doi-asserted-by":"publisher","first-page":"2889","DOI":"10.1007\/s00521-020-05126-9","volume":"33","author":"X Li","year":"2021","unstructured":"Li X, Huang L, Wei Z, Nie J, Chen Z (2021) Adaptive multi-branch correlation filters for robust visual tracking. Neural Comput Appl 33(7):2889\u20132904","journal-title":"Neural Comput Appl"},{"key":"8422_CR15","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1109\/TIP.2020.3037518","volume":"30","author":"D Yuan","year":"2020","unstructured":"Yuan D, Chang X, Huang P-Y, Liu Q, He Z (2020) Self-supervised deep correlation tracking. IEEE Trans Image Process 30:976\u2013985","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"8422_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3486678","volume":"18","author":"D Yuan","year":"2022","unstructured":"Yuan D, Chang X, Li Z, He Z (2022) Learning adaptive spatial-temporal context-aware correlation filters for uav tracking. ACM Trans Multimedia Comput Commun Appl (TOMM) 18(3):1\u201318","journal-title":"ACM Trans Multimedia Comput Commun Appl (TOMM)"},{"issue":"07","key":"8422_CR17","doi-asserted-by":"publisher","first-page":"13017","DOI":"10.1609\/aaai.v34i07.7002","volume":"34","author":"J Zhou","year":"2020","unstructured":"Zhou J, Wang P, Sun H (2020) Discriminative and robust online learning for Siamese visual tracking. Proc AAAI Conf Artif Intell 34(07):13017\u201313024. https:\/\/doi.org\/10.1609\/aaai.v34i07.7002","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8422_CR18","doi-asserted-by":"publisher","unstructured":"Gao J, Zhang T, Xu C (2019) Graph convolutional tracking. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4644\u20134654. https:\/\/doi.org\/10.1109\/CVPR.2019.00478","DOI":"10.1109\/CVPR.2019.00478"},{"key":"8422_CR19","doi-asserted-by":"publisher","unstructured":"Dai K, Zhang Y, Wang D, Li J, Lu H, Yang X (2020) High-performance long-term tracking with meta-updater. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6297\u20136306. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00633","DOI":"10.1109\/CVPR42600.2020.00633"},{"key":"8422_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03636-8","author":"L Zhou","year":"2022","unstructured":"Zhou L, Ding X, Li W, Leng J, Lei B, Yang W (2022) A location-aware Siamese network for high-speed visual tracking. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-022-03636-8","journal-title":"Appl Intell"},{"key":"8422_CR21","doi-asserted-by":"publisher","unstructured":"Wang G, Luo C, Xiong Z, Zeng W (2019) SPM-tracker: series-parallel matching for real-time visual object tracking. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 3638\u20133647. https:\/\/doi.org\/10.1109\/CVPR.2019.00376","DOI":"10.1109\/CVPR.2019.00376"},{"key":"8422_CR22","doi-asserted-by":"publisher","unstructured":"Wang Q, Zhang L, Bertinetto L, Hu W, Torr PHS (2019) Fast online object tracking and segmentation: a unifying approach. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1328\u20131338. https:\/\/doi.org\/10.1109\/CVPR.2019.00142","DOI":"10.1109\/CVPR.2019.00142"},{"key":"8422_CR23","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00661","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"8422_CR24","doi-asserted-by":"crossref","unstructured":"Cheng S, Zhong B, Li G, Liu X, Tang Z, Li X, Wang J (2021) Learning to filter: Siamese relation network for robust tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4421\u20134431","DOI":"10.1109\/CVPR46437.2021.00440"},{"key":"8422_CR25","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.neucom.2022.04.003","volume":"492","author":"K Tan","year":"2022","unstructured":"Tan K, Xu T-B, Wei Z (2022) Imsiam: IOU-aware matching-adaptive Siamese network for object tracking. Neurocomputing 492:222\u2013233. https:\/\/doi.org\/10.1016\/j.neucom.2022.04.003","journal-title":"Neurocomputing"},{"key":"8422_CR26","doi-asserted-by":"publisher","unstructured":"Zhang Z, Peng H (2019) Deeper and wider Siamese networks for real-time visual tracking. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4586\u20134595. https:\/\/doi.org\/10.1109\/CVPR.2019.00472","DOI":"10.1109\/CVPR.2019.00472"},{"key":"8422_CR27","doi-asserted-by":"publisher","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 . https:\/\/doi.org\/10.1109\/CVPR42600.2020.00670","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"8422_CR28","doi-asserted-by":"publisher","unstructured":"Zhang Z, Peng H, Fu J, Li B, Hu W (2020) Ocean: object-aware anchor-free tracking, pp 771\u2013787. https:\/\/doi.org\/10.1007\/978-3-030-58589-1_46","DOI":"10.1007\/978-3-030-58589-1_46"},{"key":"8422_CR29","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1109\/CVPR.2019.00814","DOI":"10.1109\/CVPR.2019.00814"},{"key":"8422_CR30","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neucom.2022.04.043","volume":"492","author":"H Saribas","year":"2022","unstructured":"Saribas H, Cevikalp H, K\u00f6p\u00fckl\u00fc O, Uzun B (2022) Trat: tracking by attention using spatio-temporal features. Neurocomputing 492:150\u2013161. https:\/\/doi.org\/10.1016\/j.neucom.2022.04.043","journal-title":"Neurocomputing"},{"issue":"07","key":"8422_CR31","doi-asserted-by":"publisher","first-page":"12549","DOI":"10.1609\/aaai.v34i07.6944","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 Conf Artif Intell 34(07):12549\u201312556. https:\/\/doi.org\/10.1609\/aaai.v34i07.6944","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8422_CR32","doi-asserted-by":"publisher","unstructured":"Bhat G, Danelljan M, Van\u00a0Gool L, Timofte R (2019) Learning discriminative model prediction for tracking. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 6181\u20136190. https:\/\/doi.org\/10.1109\/ICCV.2019.00628","DOI":"10.1109\/ICCV.2019.00628"},{"key":"8422_CR33","doi-asserted-by":"publisher","unstructured":"Danelljan M, Van\u00a0Gool L, Timofte R (2020) Probabilistic regression for visual tracking. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7181\u20137190. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00721","DOI":"10.1109\/CVPR42600.2020.00721"},{"issue":"7","key":"8422_CR34","doi-asserted-by":"publisher","first-page":"7713","DOI":"10.1007\/s10489-021-02768-7","volume":"52","author":"S Chen","year":"2022","unstructured":"Chen S, Qiu C, Zhang Z (2022) An efficient method for tracking failure detection using parallel correlation filtering and Siamese network. Appl Intell 52(7):7713\u20137722. https:\/\/doi.org\/10.1007\/s10489-021-02768-7","journal-title":"Appl Intell"},{"issue":"9","key":"8422_CR35","doi-asserted-by":"publisher","first-page":"9782","DOI":"10.1007\/s10489-021-03057-z","volume":"52","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Zhang Y (2022) Siamet: a Siamese based visual tracking network with enhanced templates. Appl Intell 52(9):9782\u20139794. https:\/\/doi.org\/10.1007\/s10489-021-03057-z","journal-title":"Appl Intell"},{"issue":"9","key":"8422_CR36","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. IEEE Trans Pattern Anal Mach Intell 37(9):1834\u20131848. https:\/\/doi.org\/10.1109\/TPAMI.2014.2388226","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8422_CR37","doi-asserted-by":"publisher","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 . https:\/\/doi.org\/10.1109\/ICCV.2019.00626","DOI":"10.1109\/ICCV.2019.00626"},{"key":"8422_CR38","doi-asserted-by":"publisher","unstructured":"Zhang L, Gonzalez-Garcia A, Weijer JVD, 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. https:\/\/doi.org\/10.1109\/ICCV.2019.00411","DOI":"10.1109\/ICCV.2019.00411"},{"key":"8422_CR39","unstructured":"Blatter P, Kanakis M, Danelljan M, Van\u00a0Gool L (2021) Efficient visual tracking with exemplar transformers. arXiv preprint arXiv:2112.09686"},{"key":"8422_CR40","doi-asserted-by":"crossref","unstructured":"Chen X, Wang D, Li D, Lu H (2022) Efficient visual tracking via hierarchical cross-attention transformer. arXiv preprint arXiv:2203.13537","DOI":"10.1007\/978-3-031-25085-9_26"},{"key":"8422_CR41","doi-asserted-by":"publisher","unstructured":"Yan B, Peng H, Wu K, Wang D, Fu J, Lu H (2021) Lighttrack: finding lightweight neural networks for object tracking via one-shot architecture search. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 15175\u201315184. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01493","DOI":"10.1109\/CVPR46437.2021.01493"},{"key":"8422_CR42","doi-asserted-by":"crossref","unstructured":"Zhou L, Ding X, Li W, Leng J, Lei B, Yang W (2022) A location-aware Siamese network for high-speed visual tracking. Appl Intell 1\u201317","DOI":"10.1007\/s10489-022-03636-8"},{"issue":"6","key":"8422_CR43","doi-asserted-by":"publisher","first-page":"5897","DOI":"10.1007\/s10489-021-02260-2","volume":"52","author":"L Gao","year":"2022","unstructured":"Gao L, Liu B, Fu P, Xu M, Li J (2022) Visual tracking via dynamic saliency discriminative correlation filter. Appl Intell 52(6):5897\u20135911","journal-title":"Appl Intell"},{"key":"8422_CR44","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-11009-3_1","volume-title":"Computer Vision\u2014ECCV 2018 Workshops","author":"M Kristan","year":"2019","unstructured":"Kristan M, Leonardis A, Matas J, Felsberg M (2019) The sixth visual object tracking vot2018 challenge results. In: Leal-Taix\u00e9 L, Roth S (eds) Computer Vision\u2014ECCV 2018 Workshops. Springer, Cham, pp 3\u201353"},{"key":"8422_CR45","doi-asserted-by":"publisher","unstructured":"Sun C, Wang D, Lu H, Yang M-H (2018) Correlation tracking via joint discrimination and reliability learning. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 489\u2013497. https:\/\/doi.org\/10.1109\/CVPR.2018.00058","DOI":"10.1109\/CVPR.2018.00058"},{"key":"8422_CR46","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/978-3-030-01216-8_30","volume-title":"Computer Vision\u2014ECCV 2018","author":"G Bhat","year":"2018","unstructured":"Bhat G, Johnander J, Danelljan M, Khan FS, Felsberg M (2018) Unveiling the power of deep tracking. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision\u2014ECCV 2018. Springer, Cham, pp 493\u2013509"},{"key":"8422_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01435-1","author":"T Xu","year":"2021","unstructured":"Xu T, Feng Z, Wu X-J, Kittler J (2021) Adaptive channel selection for robust visual object tracking with discriminative correlation filters. Int J Comput Vis. https:\/\/doi.org\/10.1007\/s11263-021-01435-1","journal-title":"Int J Comput Vis"},{"key":"8422_CR48","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.neucom.2022.02.027","volume":"483","author":"Y Luo","year":"2022","unstructured":"Luo Y, Xiao H, Ou J, Chen X (2022) Siamsmdfff: Siamese network tracker based on shallow-middle-deep three-level feature fusion and clustering-based adaptive rectangular window filtering. Neurocomputing 483:160\u2013170. https:\/\/doi.org\/10.1016\/j.neucom.2022.02.027","journal-title":"Neurocomputing"},{"key":"8422_CR49","doi-asserted-by":"publisher","unstructured":"Danelljan M, Bhat G, Khan FS, Felsberg M (2017) Eco: efficient convolution operators for tracking. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6931\u20136939. https:\/\/doi.org\/10.1109\/CVPR.2017.733","DOI":"10.1109\/CVPR.2017.733"},{"key":"8422_CR50","doi-asserted-by":"publisher","unstructured":"Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 4293\u20134302. https:\/\/doi.org\/10.1109\/CVPR.2016.465","DOI":"10.1109\/CVPR.2016.465"},{"key":"8422_CR51","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1109\/TMM.2021.3074239","volume":"24","author":"K Yang","year":"2022","unstructured":"Yang K, He Z, Pei W, Zhou Z, Li X, Yuan D, Zhang H (2022) Siamcorners: Siamese corner networks for visual tracking. IEEE Trans Multimedia 24:1956\u20131967. https:\/\/doi.org\/10.1109\/TMM.2021.3074239","journal-title":"IEEE Trans Multimedia"},{"issue":"5","key":"8422_CR52","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TPAMI.2019.2957464","volume":"43","author":"L Huang","year":"2021","unstructured":"Huang L, Zhao X, Huang K (2021) Got-10k: a large high-diversity benchmark for generic object tracking in the wild. IEEE Trans Pattern Anal Mach Intell 43(5):1562\u20131577. https:\/\/doi.org\/10.1109\/TPAMI.2019.2957464","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8422_CR53","doi-asserted-by":"publisher","unstructured":"Yang T, Xu P, Hu R, Chai H, Chan AB (2020) Roam: recurrently optimizing tracking model. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 6717\u20136726 . https:\/\/doi.org\/10.1109\/CVPR42600.2020.00675","DOI":"10.1109\/CVPR42600.2020.00675"},{"key":"8422_CR54","doi-asserted-by":"crossref","unstructured":"Xing D, Evangeliou N, Tsoukalas A, Tzes A (2022) Siamese transformer pyramid networks for real-time UAV tracking. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 2139\u20132148","DOI":"10.1109\/WACV51458.2022.00196"},{"key":"8422_CR55","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/978-3-030-01246-5_19","volume-title":"Computer Vision\u2014ECCV 2018","author":"M M\u00fcller","year":"2018","unstructured":"M\u00fcller M, Bibi A, Giancola S, Alsubaihi S, Ghanem B (2018) Trackingnet: a large-scale dataset and benchmark for object tracking in the wild. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision\u2014ECCV 2018. Springer, Cham, pp 310\u2013327"},{"key":"8422_CR56","doi-asserted-by":"publisher","unstructured":"Galoogahi HK, Fagg A, Huang C, Ramanan D, Lucey S (2017) Need for speed: a benchmark for higher frame rate object tracking. In: 2017 IEEE international conference on computer vision (ICCV), pp 1134\u20131143. https:\/\/doi.org\/10.1109\/ICCV.2017.128","DOI":"10.1109\/ICCV.2017.128"},{"key":"8422_CR57","doi-asserted-by":"crossref","unstructured":"Danelljan M, Robinson A, Khan F, Felsberg M (2016) Beyond correlation filters: learning continuous convolution operators for visual tracking","DOI":"10.1109\/CVPR.2017.733"},{"issue":"5","key":"8422_CR58","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TPAMI.2018.2828817","volume":"41","author":"Y Qi","year":"2019","unstructured":"Qi Y, Zhang S, Qin L, Huang Q, Yao H, Lim J, Yang M-H (2019) Hedging deep features for visual tracking. IEEE Trans Pattern Anal Mach Intell 41(5):1116\u20131130. https:\/\/doi.org\/10.1109\/TPAMI.2018.2828817","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8422_CR59","doi-asserted-by":"publisher","unstructured":"Wang L, Ouyang W, Wang X, Lu H (2015) Visual tracking with fully convolutional networks. In: 2015 IEEE international conference on computer vision (ICCV), pp 3119\u20133127. https:\/\/doi.org\/10.1109\/ICCV.2015.357","DOI":"10.1109\/ICCV.2015.357"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08422-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08422-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08422-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T17:34:24Z","timestamp":1683826464000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08422-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,4]]},"references-count":59,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["8422"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08422-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,4]]},"assertion":[{"value":"1 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2023","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"}}]}}