{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T21:09:16Z","timestamp":1758402556329,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2033218, 61831018"],"award-info":[{"award-number":["U2033218, 61831018"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s10489-023-04839-3","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T10:02:42Z","timestamp":1692784962000},"page":"26439-26453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatial graph attention network-based object tracking with adaptive cosine window"],"prefix":"10.1007","volume":"53","author":[{"given":"Liu-Yi","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Yan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong-Bin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"4839_CR1","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 (CVPR)","DOI":"10.1109\/CVPR.2018.00935"},{"key":"4839_CR2","doi-asserted-by":"crossref","unstructured":"Wang Q, Zhang L, Bertinetto L, Hu W, Torr PH (2019) Fast online object tracking and segmentation: A unifying approach. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00142"},{"key":"4839_CR3","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 (CVPR)","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"4839_CR4","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 (CVPR)","DOI":"10.1109\/CVPR46437.2021.00440"},{"issue":"1","key":"4839_CR5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Transactions on Neural Networks 20(1):61\u201380","journal-title":"IEEE Transactions on Neural Networks"},{"key":"4839_CR6","doi-asserted-by":"crossref","unstructured":"Zhang S, Tong H, Xu J, Maciejewski R (2019) Graph convolutional networks: a comprehensive review. Computational Social Networks 6(1):1\u201323","DOI":"10.1186\/s40649-019-0069-y"},{"issue":"1","key":"4839_CR7","first-page":"4","volume":"1050","author":"P Velickovic","year":"2018","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. Stat 1050(1):4","journal-title":"Stat"},{"key":"4839_CR8","doi-asserted-by":"crossref","unstructured":"Gao J, Zhang T, Xu C (2019) Graph convolutional tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00478"},{"key":"4839_CR9","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 (CVPR)","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"4839_CR10","doi-asserted-by":"crossref","unstructured":"Huang, L. Zhao, X. Huang, K. (2019) Got-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Vol: 43(5), 1562\u20131577","DOI":"10.1109\/TPAMI.2019.2957464"},{"key":"4839_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 (CVPR)","DOI":"10.1109\/CVPR.2013.312"},{"key":"4839_CR12","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 (CVPR)","DOI":"10.1109\/CVPR.2019.00552"},{"key":"4839_CR13","doi-asserted-by":"crossref","unstructured":"Cen M, Jung C (2018) Fully convolutional siamese fusion networks for object tracking. In: 2018 25Th IEEE International Conference on Image Processing (ICIP)","DOI":"10.1109\/ICIP.2018.8451102"},{"key":"4839_CR14","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)","DOI":"10.1007\/978-3-030-01240-3_7"},{"key":"4839_CR15","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 (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4839_CR16","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 (CVPR)","DOI":"10.1109\/CVPR.2019.00441"},{"key":"4839_CR17","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 (AIII)","DOI":"10.1609\/aaai.v34i07.6944"},{"key":"4839_CR18","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 (CVPR)","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"4839_CR19","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 (CVPR)","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"4839_CR20","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 (CVPR)","DOI":"10.1109\/CVPR52688.2022.00854"},{"key":"4839_CR21","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 (CVPR)","DOI":"10.1109\/CVPR.2019.00479"},{"key":"4839_CR22","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 (ICCV)","DOI":"10.1109\/ICCV.2019.00628"},{"issue":"8","key":"4839_CR23","first-page":"1","volume":"33","author":"S Xiao","year":"2022","unstructured":"Xiao S, Wang S, Dai Y, Guo W (2022) Graph neural networks in node classification: survey and evaluation. Machine Vision and Applications 33(8):1\u201319","journal-title":"Machine Vision and Applications"},{"issue":"2","key":"4839_CR24","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s12652-021-03324-4","volume":"14","author":"A Subasi","year":"2023","unstructured":"Subasi A, Dogan S, Tuncer T (2023) novel automated tower graph ased ecg signal classification method with hexadecimal local adaptive inary pattern and deep learning. Journal of Ambient Intelligence and umanized Computing 14(2):711\u2013725","journal-title":"Journal of Ambient Intelligence and umanized Computing"},{"issue":"1","key":"4839_CR25","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TIP.2022.3144017","volume":"31","author":"Y Dong","year":"2022","unstructured":"Dong Y, Liu Q, Du B, Zhang L (2022) eighted feature fusion of convolutional neural network and graph attention network for hyperspectral mage classification. IEEE Transactions on Image Processing 31(1):1559\u20131572","journal-title":"IEEE Transactions on Image Processing"},{"issue":"12","key":"4839_CR26","doi-asserted-by":"publisher","first-page":"16427","DOI":"10.1002\/er.8307","volume":"46","author":"P Takyi-Aninakwa","year":"2022","unstructured":"Takyi-Aninakwa P, Wang S, Zhang H, Appiah E, Bobobee ED, Fernandez C (2022) A strong tracking adaptive fading-extended kalman filter for the state of charge estimation of lithium-ion batteries. Int J Energy Res 46(12):16427\u201316444","journal-title":"Int J Energy Res"},{"issue":"1","key":"4839_CR27","first-page":"931","volume":"64","author":"K Alaaudeen","year":"2022","unstructured":"Alaaudeen K, Aruna T, Ananthi G (2022) An improved strong tracking kalman filter algorithm for real-time vehicle tracking. Materials Today: Proceedings 64(1):931\u2013939","journal-title":"Materials Today: Proceedings"},{"issue":"12","key":"4839_CR28","doi-asserted-by":"publisher","first-page":"16427","DOI":"10.1002\/er.8307","volume":"46","author":"P Takyi-Aninakwa","year":"2022","unstructured":"Takyi-Aninakwa P, Wang S, Zhang H, Appiah E, Bobobee ED, Fernandez C (2022) A strong tracking adaptive fading-extended kalman filter for the state of charge estimation of lithium-ion batteries. Int J Energy Res 46(12):16427\u201316444","journal-title":"Int J Energy Res"},{"key":"4839_CR29","doi-asserted-by":"crossref","unstructured":"Fang W, Zhuo W, Yan J, Song Y, Jiang D, Zhou T (2022) Attention meets long short-term memory: A deep learning network for traffic flow forecasting. Physica A: Statistical Mechanics and its Applications 587(1)","DOI":"10.1016\/j.physa.2021.126485"},{"key":"4839_CR30","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 (ECCV)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"4839_CR31","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L.-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"4839_CR32","doi-asserted-by":"crossref","unstructured":"Real E, Shlens J, Mazzocchi S, Pan X, Vanhoucke V (2017) Youtubeboundingboxes: A large high-precision human-annotated data set for object detection in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2017.789"},{"key":"4839_CR33","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 (ECCV)","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"4839_CR34","doi-asserted-by":"crossref","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"4839_CR35","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 (CVPR)","DOI":"10.1109\/CVPR42600.2020.00721"},{"key":"4839_CR36","doi-asserted-by":"crossref","unstructured":"Fu Z, Liu Q, Fu Z, Wang Y (2021) Stmtrack: Template-free visual tracking with space-time memory networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR46437.2021.01356"},{"key":"4839_CR37","doi-asserted-by":"crossref","unstructured":"Zhang Z, Peng H, Fu J, Li B, Hu W (2020) Ocean: Object-aware anchor-free tracking. In: European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-030-58589-1_46"},{"key":"4839_CR38","doi-asserted-by":"crossref","unstructured":"Du F, Liu P, Wei Z, 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 (CVPR)","DOI":"10.1109\/CVPR42600.2020.00687"},{"key":"4839_CR39","doi-asserted-by":"crossref","unstructured":"Zhang D, Zheng Z, Jia R, Li M (2021) Visual tracking via hierarchical deep reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v35i4.16443"},{"key":"4839_CR40","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 (CVPR)","DOI":"10.1109\/CVPR42600.2020.00716"},{"key":"4839_CR41","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. In: International Joint Conference on Artificial Intelligence (IJCAI)","DOI":"10.24963\/ijcai.2021\/132"},{"key":"4839_CR42","doi-asserted-by":"crossref","unstructured":"Yan B, Peng H, Fu J, Wang D, Lu H (2021) Learning spatio-temporal transformer for visual tracking. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV48922.2021.01028"},{"issue":"20","key":"4839_CR43","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.ins.2022.11.055","volume":"619","author":"E Di Nardo","year":"2023","unstructured":"Di Nardo E, Ciaramella A (2023) Tracking vision transformer with class and regression tokens. Information Sciences 619(20):276\u2013287","journal-title":"Information Sciences"},{"key":"4839_CR44","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 (ICCV)","DOI":"10.1109\/ICCV48922.2021.00971"},{"key":"4839_CR45","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 (CVPR)","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"4839_CR46","doi-asserted-by":"crossref","unstructured":"Cui Y, Jiang C, Wang L, Wu G (2022) Mixformer: End-to-end tracking with iterative mixed attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR52688.2022.01324"},{"key":"4839_CR47","doi-asserted-by":"crossref","unstructured":"Zheng, Z. Wan, Y. Zhang, Y. Xiang, S. Peng, D. Zhang, B. (2021) Clnet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) Vol: 175(1), 247\u2013267","DOI":"10.1016\/j.isprsjprs.2021.03.005"},{"key":"4839_CR48","doi-asserted-by":"crossref","unstructured":"Zheng J, Ma C, Peng H, Yang X (2021) Learning to track objects from unlabeled videos. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV48922.2021.01329"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04839-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04839-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04839-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T15:07:24Z","timestamp":1698246444000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04839-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,23]]},"references-count":48,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["4839"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04839-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,8,23]]},"assertion":[{"value":"25 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}