{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:16:17Z","timestamp":1774541777241,"version":"3.50.1"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61861032"],"award-info":[{"award-number":["61861032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Siamese-based trackers have been widely studied for their high accuracy and speed. Both the feature extraction and feature fusion are two important components in Siamese-based trackers. Siamese-based trackers obtain fine local features by traditional convolution. However, some important channel information and global information are lost when enhancing local features. In the feature fusion process, cross-correlation-based feature fusion between the template and search region feature ignores the global spatial context information and does not make the best of the spatial information. In this paper, to solve the above problem, we design a novel feature extraction sub-network based on batch-free normalization re-parameterization convolution, which scales the features in the channel dimension and increases the receptive field. Richer channel information is obtained and powerful target features are extracted for the feature fusion. Furthermore, we learn a feature fusion network (FFN) based on feature filter. The FFN fuses the template and search region features in a global spatial context to obtain high-quality fused features by enhancing important features and filtering redundant features. By jointly learning the proposed feature extraction sub-network and FFN, the local and global information are fully exploited. Then, we propose a novel tracking algorithm based on the designed feature extraction sub-network and FFN with re-parameterization convolution and feature filter, referred to as RCFT. We evaluate the proposed RCFT tracker and some recent state-of-the-art (SOTA) trackers on OTB100, VOT2018, LaSOT, GOT-10k, UAV123 and the visual-thermal dataset VOT-RGBT2019 datasets, which achieves superior tracking performance with 45 FPS tracking speed.<\/jats:p>","DOI":"10.1007\/s40747-023-01223-z","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T04:01:56Z","timestamp":1694750516000},"page":"1501-1515","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["RCFT: re-parameterization convolution and feature filter for object tracking"],"prefix":"10.1007","volume":"10","author":[{"given":"Yuanyun","family":"Wang","sequence":"first","affiliation":[]},{"given":"Wenhui","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Yin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6750-5105","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"1223_CR1","doi-asserted-by":"crossref","unstructured":"Xu L, Kim P, Wang M, Pan J, Yang X (2022) Gao M (2022) Spatio-temporal joint aberrance suppressed correlation filter for visual tracking. Complex & Intelligent Systems 8:3765\u20133777","DOI":"10.1007\/s40747-021-00544-1"},{"key":"1223_CR2","doi-asserted-by":"crossref","unstructured":"Bolme DS, Beveridge JR, Draper BA, LuiYM (2010)Visual object tracking using adaptive correlation filters. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2544\u20132550","DOI":"10.1109\/CVPR.2010.5539960"},{"issue":"4","key":"1223_CR3","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1007\/s40747-020-00161-4","volume":"7","author":"S Liu","year":"2021","unstructured":"Liu S, Liu D, Srivastava G, Po\u0142ap D, Wo\u017aniak M (2021) Overview and methods of correlation filter algorithms in object tracking. Complex Intell Syst 7(4):1895\u20131917","journal-title":"Complex Intell Syst"},{"key":"1223_CR4","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Shahbaz\u00a0Khan F, Felsberg M (2017) Eco: efficient convolution operators for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6638\u20136646","DOI":"10.1109\/CVPR.2017.733"},{"key":"1223_CR5","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional Siamese networks for object tracking. In: European conference on computer vision. Springer, Berlin, pp 850\u2013865","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"1223_CR6","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 IEEE conference on computer vision and pattern recognition, pp 8971\u20138980","DOI":"10.1109\/CVPR.2018.00935"},{"key":"1223_CR7","doi-asserted-by":"crossref","unstructured":"Guo D, Wang J, Cui Y, Wang Z, Chen S (2020) SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6269\u20136277","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"1223_CR8","doi-asserted-by":"crossref","unstructured":"Hu M, Feng J, Hua J, Lai B, Huang J, Gong X, Hua X-S (2022) Online convolutional re-parameterization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 568\u2013577","DOI":"10.1109\/CVPR52688.2022.00065"},{"key":"1223_CR9","doi-asserted-by":"crossref","unstructured":"Huang L, Zhou Y, Wang T, Luo J, Liu X (2022) Delving into the estimation shift of batch normalization in a network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 763\u2013772","DOI":"10.1109\/CVPR52688.2022.00084"},{"key":"1223_CR10","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1223_CR11","doi-asserted-by":"crossref","unstructured":"Wu Y, Lim J, Yang M-H (2013) Online object tracking: a benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2411\u20132418","DOI":"10.1109\/CVPR.2013.312"},{"key":"1223_CR12","unstructured":"Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, \u010cehovin\u00a0Zajc L, Vojir T, Bhat G, Lukezic A, Eldesokey A et al (2018) The sixth visual object tracking vot2018 challenge results. In: Proceedings of the European conference on computer vision (ECCV) workshops"},{"key":"1223_CR13","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 (2019) Lasot: a high-quality benchmark for large-scale single object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5374\u20135383","DOI":"10.1109\/CVPR.2019.00552"},{"issue":"5","key":"1223_CR14","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","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1223_CR15","doi-asserted-by":"crossref","unstructured":"Mueller M, Smith N, Ghanem B (2016) A benchmark and simulator for UAV tracking. In: European conference on computer vision. Springer, Berlin, pp 445\u2013461","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"1223_CR16","unstructured":"Kristan M, Matas J, Leonardis A, Felsberg M, Pflugfelder R, Kamarainen J-K, Cehovin\u00a0Zajc L, Drbohlav O, Lukezic A, Berg A et\u00a0al (2019) The seventh visual object tracking vot2019 challenge results. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops"},{"key":"1223_CR17","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Khan FS, Felsberg M (2019) Atom: accurate tracking by overlap maximization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4660\u20134669","DOI":"10.1109\/CVPR.2019.00479"},{"key":"1223_CR18","doi-asserted-by":"crossref","unstructured":"Bhat G, Danelljan M, Gool LV, Timofte R (2019) Learning discriminative model prediction for tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6182\u20136191","DOI":"10.1109\/ICCV.2019.00628"},{"key":"1223_CR19","doi-asserted-by":"crossref","unstructured":"Danelljan M, Gool LV, Timofte R (2020) Probabilistic regression for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7183\u20137192","DOI":"10.1109\/CVPR42600.2020.00721"},{"key":"1223_CR20","doi-asserted-by":"crossref","unstructured":"Bhat G, Danelljan M, Van\u00a0Gool L, Timofte R (2020) Know your surroundings: exploiting scene information for object tracking. In: European conference on computer vision. Springer, Berlin, pp 205\u2013221","DOI":"10.1007\/978-3-030-58592-1_13"},{"key":"1223_CR21","doi-asserted-by":"crossref","unstructured":"Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PH (2017) End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2805\u20132813","DOI":"10.1109\/CVPR.2017.531"},{"key":"1223_CR22","doi-asserted-by":"crossref","unstructured":"Guo Q, Feng W, Zhou C, Huang R, Wan L, Wang S (2017) Learning dynamic Siamese network for visual object tracking. In: Proceedings of the IEEE international conference on computer vision, pp 1763\u20131771","DOI":"10.1109\/ICCV.2017.196"},{"key":"1223_CR23","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 preprint. arXiv:2204.14093","DOI":"10.1109\/TMM.2022.3206668"},{"key":"1223_CR24","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 European conference on computer vision (ECCV), pp 101\u2013117","DOI":"10.1007\/978-3-030-01240-3_7"},{"key":"1223_CR25","doi-asserted-by":"crossref","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: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4282\u20134291","DOI":"10.1109\/CVPR.2019.00441"},{"key":"1223_CR26","doi-asserted-by":"crossref","unstructured":"Zhang Z, Peng H (2019) Deeper and wider Siamese networks for real-time visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4591\u20134600","DOI":"10.1109\/CVPR.2019.00472"},{"key":"1223_CR27","doi-asserted-by":"crossref","unstructured":"Voigtlaender P, Luiten J, Torr PH, Leibe B (2020) Siam R-CNN: visual tracking by re-detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6578\u20136588","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"1223_CR28","doi-asserted-by":"crossref","unstructured":"J.\u00a0Peng, Z.\u00a0Jiang, Y.\u00a0Gu, Y.\u00a0Wu, Y.\u00a0Wang, Y.\u00a0Tai, C.\u00a0Wang, W.\u00a0Lin, Siamrcr: Reciprocal classification and regression for visual object tracking, arXiv preprint arXiv:2105.11237 (2021)","DOI":"10.24963\/ijcai.2021\/132"},{"key":"1223_CR29","doi-asserted-by":"crossref","unstructured":"Xu Y, Wang Z, Li Z, Yuan Y, Yu G (2020) Siamfc++: towards robust and accurate visual tracking with target estimation guidelines. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a034, pp 12549\u201312556","DOI":"10.1609\/aaai.v34i07.6944"},{"key":"1223_CR30","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhong B, Li G, Zhang S, Ji R (2020) Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6668\u20136677","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"1223_CR31","doi-asserted-by":"crossref","unstructured":"Zhang Z, Peng H, Fu J, Li B, Hu W (2020) Ocean: object-aware anchor-free tracking. In: Computer vision\u2014ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, proceedings, part XXI 16. Springer, Berlin, pp 771\u2013787","DOI":"10.1007\/978-3-030-58589-1_46"},{"key":"1223_CR32","doi-asserted-by":"crossref","unstructured":"Wang Q, Teng Z, Xing J, Gao J, Hu W, Maybank S (2018) Learning attentions: residual attentional Siamese network for high performance online visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4854\u20134863","DOI":"10.1109\/CVPR.2018.00510"},{"key":"1223_CR33","doi-asserted-by":"crossref","unstructured":"Deng S, Liang Z, Sun L, Jia K (2022) Vista: boosting 3d object detection via dual cross-view spatial attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8448\u20138457","DOI":"10.1109\/CVPR52688.2022.00826"},{"key":"1223_CR34","doi-asserted-by":"crossref","unstructured":"Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3156\u20133164","DOI":"10.1109\/CVPR.2017.683"},{"key":"1223_CR35","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1223_CR36","unstructured":"Guo G, Wang H, Yan Y, Liao H-YM, Li B (2018) A new target-specific object proposal generation method for visual tracking. arXiv preprint. arXiv:1803.10098"},{"key":"1223_CR37","doi-asserted-by":"crossref","unstructured":"Avytekin C, Cricri F, Aksu E (2018) Saliency enhanced robust visual tracking. In: European workshop on visual information processing (EUVIP) arXiv:1802.02783, pp 1\u20135","DOI":"10.1109\/EUVIP.2018.8611706"},{"key":"1223_CR38","doi-asserted-by":"crossref","unstructured":"Zhu Z, Wu W, Zou W, Yan J (2018) End-to-end flow correlation tracking with spatial-temporal attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 548\u2013557","DOI":"10.1109\/CVPR.2018.00064"},{"key":"1223_CR39","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":"1223_CR40","doi-asserted-by":"crossref","unstructured":"Chen X, Yan B, Zhu J, Wang D, Yang X, Lu H (2021) Transformer tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8126\u20138135","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"1223_CR41","doi-asserted-by":"crossref","unstructured":"Wang N, Zhou W, Wang J, Li H (2021) Transformer meets tracker: exploiting temporal context for robust visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1571\u20131580","DOI":"10.1109\/CVPR46437.2021.00162"},{"key":"1223_CR42","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"1223_CR43","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint. arXiv:1409.1556"},{"key":"1223_CR44","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"1223_CR45","doi-asserted-by":"crossref","unstructured":"Cao Z, Huang Z, Pan L, Zhang S, Liu Z, Fu C (2022) Tctrack: temporal contexts for aerial tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14798\u201314808","DOI":"10.1109\/CVPR52688.2022.01438"},{"key":"1223_CR46","doi-asserted-by":"crossref","unstructured":"Dong X, Shen J (2018) Triplet loss in Siamese network for object tracking. In: Proceedings of the European conference on computer vision (ECCV), pp 459\u2013474","DOI":"10.1007\/978-3-030-01261-8_28"},{"key":"1223_CR47","doi-asserted-by":"crossref","unstructured":"Guo D, Shao Y, Cui Y, Wang Z, Zhang L, Shen C (2021) Graph attention tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9543\u20139552","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"1223_CR48","unstructured":"Zhao M, Okada K, Inaba M (2021) Trtr: visual tracking with transformer. arXiv preprint. arXiv:2105.03817"},{"key":"1223_CR49","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, Berlin, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1223_CR50","doi-asserted-by":"crossref","unstructured":"Muller M, Bibi A, Giancola S, Alsubaihi S, Ghanem B (2018) Trackingnet: a large-scale dataset and benchmark for object tracking in the wild. In: Proceedings of the European conference on computer vision (ECCV), pp 300\u2013317","DOI":"10.1007\/978-3-030-01246-5_19"},{"key":"1223_CR51","doi-asserted-by":"crossref","unstructured":"Gao S, Zhou C, Ma C, Wang X, Yuan J (2022) Aiatrack: attention in attention for transformer visual tracking. arXiv preprint. arXiv:2207.09603","DOI":"10.1007\/978-3-031-20047-2_9"},{"key":"1223_CR52","doi-asserted-by":"crossref","unstructured":"Tang F, Ling Q (2022) Ranking-based Siamese visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8741\u20138750","DOI":"10.1109\/CVPR52688.2022.00854"},{"key":"1223_CR53","doi-asserted-by":"crossref","unstructured":"Mayer C, Danelljan M, Bhat G, Paul M, Paudel DP, Yu F, Van\u00a0Gool L (2022) Transforming model prediction for tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8731\u20138740","DOI":"10.1109\/CVPR52688.2022.00853"},{"key":"1223_CR54","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1109\/TIP.2020.3038356","volume":"30","author":"S Pu","year":"2020","unstructured":"Pu S, Song Y, Ma C, Zhang H, Yang M-H (2020) Learning recurrent memory activation networks for visual tracking. IEEE Trans Image Process 30:725\u2013738","journal-title":"IEEE Trans Image Process"},{"key":"1223_CR55","doi-asserted-by":"crossref","unstructured":"Shen Q, Qiao L, Guo J, Li P, Li X, Li B, Feng W, Gan W, Wu W, Ouyang W (2022) Unsupervised learning of accurate Siamese tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8101\u20138110","DOI":"10.1109\/CVPR52688.2022.00793"},{"key":"1223_CR56","doi-asserted-by":"crossref","unstructured":"Zhou Z, Pei W, Li X, Wang H, Zheng F, He Z (2021) Saliency-associated object tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9866\u20139875","DOI":"10.1109\/ICCV48922.2021.00972"},{"key":"1223_CR57","doi-asserted-by":"crossref","unstructured":"Du F, Liu P, Zhao W, Tang X (2020) Correlation-guided attention for corner detection based visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6836\u20136845","DOI":"10.1109\/CVPR42600.2020.00687"},{"key":"1223_CR58","doi-asserted-by":"crossref","unstructured":"Huang L, Zhao X, Huang K (2020) Globaltrack: a simple and strong baseline for long-term tracking. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a034, pp 11037\u201311044","DOI":"10.1609\/aaai.v34i07.6758"},{"key":"1223_CR59","doi-asserted-by":"crossref","unstructured":"Lukezic A, Matas J, Kristan M (2020) D3s-a discriminative single shot segmentation tracker. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7133\u20137142","DOI":"10.1109\/CVPR42600.2020.00716"},{"key":"1223_CR60","doi-asserted-by":"crossref","unstructured":"Song Y, Ma C, Wu X, Gong L, Bao L, Zuo W, Shen C, Lau RW, Yang M-H (2018) Vital: visual tracking via adversarial learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8990\u20138999","DOI":"10.1109\/CVPR.2018.00937"},{"key":"1223_CR61","doi-asserted-by":"crossref","unstructured":"Ma F, Shou MZ, Zhu L, Fan H, Xu Y, Yang Y, Yan Z (2022) Unified transformer tracker for object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8781\u20138790","DOI":"10.1109\/CVPR52688.2022.00858"},{"key":"1223_CR62","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":"1223_CR63","doi-asserted-by":"crossref","unstructured":"Yu B, Tang M, Zheng L, Zhu G, Wang J, Feng H, Feng X, Lu H (2021) High-performance discriminative tracking with transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9856\u20139865","DOI":"10.1109\/ICCV48922.2021.00971"},{"key":"1223_CR64","doi-asserted-by":"crossref","unstructured":"Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. In: European conference on computer vision. Springer, Berlin, pp 234\u2013247","DOI":"10.1007\/978-3-540-88682-2_19"},{"key":"1223_CR65","doi-asserted-by":"crossref","unstructured":"Wang G, Luo C, Xiong Z, Zeng W (2019) Spm-tracker: series-parallel matching for real-time visual object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3643\u20133652","DOI":"10.1109\/CVPR.2019.00376"},{"key":"1223_CR66","doi-asserted-by":"crossref","unstructured":"Cao Z, Fu C, Ye J, Li B, Li Y (2021) Hift: hierarchical feature transformer for aerial tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 15457\u201315466","DOI":"10.1109\/ICCV48922.2021.01517"},{"issue":"1","key":"1223_CR67","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TIV.2020.2980735","volume":"6","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Li C, Tang J, Luo B (2020) Quality-aware feature aggregation network for robust RGBT tracking. IEEE Trans Intell Veh 6(1):121\u2013130","journal-title":"IEEE Trans Intell Veh"},{"issue":"2","key":"1223_CR68","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1109\/TCSVT.2021.3067997","volume":"32","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Li C, Tang J, Luo B, Wang L (2021) RGBT tracking by trident fusion network. IEEE Trans Circuits Syst Video Technol 32(2):579\u2013592","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1223_CR69","doi-asserted-by":"crossref","unstructured":"Yu Y, Xiong Y, Huang W, Scott M. R (2020) Deformable siamese attention networks for visual object tracking, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6728\u20136737","DOI":"10.1109\/CVPR42600.2020.00676"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01223-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01223-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01223-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T22:36:32Z","timestamp":1707604592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01223-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,15]]},"references-count":69,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1223"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01223-z","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,15]]},"assertion":[{"value":"30 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 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"}},{"value":"We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We understand that the corresponding author is the sole contact for the editorial process. He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Author agreement"}}]}}