{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:45:43Z","timestamp":1740149143298,"version":"3.37.3"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2022JQ-677"],"award-info":[{"award-number":["2022JQ-677"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Due to the ignoring of rich spatio-temporal and global contextual information with convolutional neural networks in features extraction, the traditional method is prone to tracking drift or even failure in complex scenario, especially for the tiny targets in aerial photography scenario. In this work, it proposes a transformer feature integration network (TFITrack) to obtain diverse and comprehensive target feature for the robust object tracking. Based on the typical transformer architecture, it optimizes encoder and decoder structure for aggregating discriminative spatio-temporal information and global context-awareness feature. Furthermore, the encoder introduces the similarity calculation layer and dual-attention module; the aim is to deepen the similarity between features and make corrections for channel and spatial dimensions, and feature representation is improved. Finally, with the introduction of the temporal context filtering layer, unimportant feature information is ignored adaptively, obtaining a balance between the parameters number reduction and stable performance. Experimental results show that the proposed tracking algorithm exhibits excellent tracking performance on seven benchmark datasets, especially on the aerial dataset UAV123, UAV20L, and UAV123@10fps, which presents the advantages of the novel method in dealing with fast motion and external interference.<\/jats:p>","DOI":"10.1007\/s44196-024-00500-0","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T10:02:05Z","timestamp":1714384925000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TFITrack: Transformer Feature Integration Network for Object Tracking"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6326-7130","authenticated-orcid":false,"given":"Xiuhua","family":"Hu","sequence":"first","affiliation":[]},{"given":"Huan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shuang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Hui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"500_CR1","doi-asserted-by":"publisher","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: Fully-convolutional siamese networks for object tracking. In: Proceedings of the 2016 European Conference on Computer Vision, ECCV 2016, Amsterdam, The Netherlands, October 8\u201316, 2016, pp. 850\u2013865 (2016).https:\/\/doi.org\/10.48550\/arXiv.1606.09549","DOI":"10.48550\/arXiv.1606.09549"},{"key":"500_CR2","doi-asserted-by":"publisher","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with Siamese region proposal network. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt, Lake, City, UT, USA, June18\u201322, 2018, pp. 8971\u20138980 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00935","DOI":"10.1109\/CVPR.2018.00935"},{"key":"500_CR3","doi-asserted-by":"publisher","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: Siamrpn++: Evolution of Siamese visual tracking with very deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June16\u201320, 2019, pp. 4282\u20134291 (2019). https:\/\/doi.org\/10.48550\/arXiv.1812.11703","DOI":"10.48550\/arXiv.1812.11703"},{"key":"500_CR4","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Peng, H.: Deeper and wider Siamese networks for real-time visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 15\u201320, 2019, pp. 4591\u20134600 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00472","DOI":"10.1109\/CVPR.2019.00472"},{"key":"500_CR5","doi-asserted-by":"publisher","unstructured":"Fan, H., Ling, H.: Siamese cascaded region proposal networks for real-time visual tracking. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, Long Beach, CA, USA, June 15\u201320, 2018, pp. 7952\u20137961 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00814","DOI":"10.1109\/CVPR.2019.00814"},{"key":"500_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119797","volume":"653","author":"H Chen","year":"2024","unstructured":"Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: NT-DPTC: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Inf. Sci. 653, 119797 (2024). https:\/\/doi.org\/10.1016\/j.ins.2023.119797","journal-title":"Inf. Sci."},{"key":"500_CR7","doi-asserted-by":"publisher","unstructured":"Yu, Y., Xiong, Y., Huang, W., Scott, M.R.: Deformable Siamese attention networks for visual object tracking. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June13\u201319, 2020, pp. 6728\u20136737 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00676","DOI":"10.1109\/CVPR42600.2020.00676"},{"key":"500_CR8","doi-asserted-by":"publisher","unstructured":"Cao, Z., Fu, C., Ye, J., Li, B., Li, Y.: SiamAPN++: Siamese attentional aggregation network for real-time UAV tracking. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, Prague, Czech Republic, September 27\u2013October 01, 2021, pp. 3086\u20133092 (2021). https:\/\/doi.org\/10.1109\/IROS51168.2021.9636309","DOI":"10.1109\/IROS51168.2021.9636309"},{"key":"500_CR9","doi-asserted-by":"publisher","unstructured":"Cao, Z., Fu, C., Ye, J., Li, B., Li, Y.: HiFT: Hierarchical Feature Transformer for Aerial Tracking. In: IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10\u201317, 2021, pp. 15437\u201315446 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01517","DOI":"10.1109\/ICCV48922.2021.01517"},{"key":"500_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111466","volume":"288","author":"L Hu","year":"2024","unstructured":"Hu, L., Wang, Z., Li, H., Wu, P., Mao, J., Zeng, N.: \u2113-DARTS: Light-weight differentiable architecture search with robustness enhancement strategy. Knowl. Based. Syst. 288, 111466 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.111466","journal-title":"Knowl. Based. Syst."},{"key":"500_CR11","doi-asserted-by":"publisher","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, December 4\u20139, 2017, pp. 5998\u20136008 (2017). https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"500_CR12","doi-asserted-by":"publisher","unstructured":"Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., Lu. H.: Transformer tracking. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Nashville, TN, USA, June20\u201325, 2021, pp. 8122\u20138131 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00803","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"500_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44196-023-00360-0","volume":"16","author":"L Fan","year":"2023","unstructured":"Fan, L., Kim, P.: Dual Siamese anchor points adaptive tracker with transformer for RGBT tracking. Int J. Comput. Intell. Syst. 16, 1\u201318 (2023). https:\/\/doi.org\/10.1007\/s44196-023-00360-0","journal-title":"Int J. Comput. Intell. Syst."},{"key":"500_CR14","doi-asserted-by":"publisher","unstructured":"Yan, B., Peng, H., Fu, J., Wang, D., Lu, H.: Learning spatio-temporal transformer for visual tracking. In: IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10\u201317, 2021, pp. 10428\u201310437 (2021). https:\/\/doi.org\/10.48550\/arXiv.2103.17154","DOI":"10.48550\/arXiv.2103.17154"},{"key":"500_CR15","doi-asserted-by":"publisher","unstructured":"Cao, Z., Huang, Z., Pan, L., Zhang, S., Liu, Z., Fu, C.: TCTrack: Temporal Contexts for Aerial Tracking. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June18\u201324, 2022, pp. 14778\u201314788 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01438","DOI":"10.1109\/CVPR52688.2022.01438"},{"key":"500_CR16","doi-asserted-by":"publisher","unstructured":"Blatter, P., Kanakis, M., Danelljan, M., Gool, L.V.: Efficient Visual Tracking with Exemplar Transformers (2021). https:\/\/doi.org\/10.48550\/arXiv.2112.09686","DOI":"10.48550\/arXiv.2112.09686"},{"key":"500_CR17","doi-asserted-by":"publisher","unstructured":"Huang, T., Huang, L., You, S., Wang, F., Qian, C., Xu, C.: LightViT: Towards Light-Weight Convolution-Free Vision Transformers (2022). https:\/\/doi.org\/10.48550\/arXiv.2207.05557","DOI":"10.48550\/arXiv.2207.05557"},{"key":"500_CR18","doi-asserted-by":"publisher","unstructured":"Wang, Q., Teng, Z., Xing, J., Gao, J., Hu, W., Maybank, S.: Learning attentions: residual attentional Siamese network for high performance online visual tracking. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18\u201323, 2018, pp. 4854\u20134863 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00510","DOI":"10.1109\/CVPR.2018.00510"},{"key":"500_CR19","doi-asserted-by":"publisher","unstructured":"He, A., Luo, C., Tian, X., Zeng, W.: A twofold Siamese network for real-time object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, Anchorage, AK, USA, June23\u201328, 2008, pp.4834\u20134843 (2008). https:\/\/doi.org\/10.1109\/CVPR.2018.00508","DOI":"10.1109\/CVPR.2018.00508"},{"issue":"2","key":"500_CR20","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/JAS.2023.124029","volume":"11","author":"N Zeng","year":"2024","unstructured":"Zeng, N., Li, X., Wu, P., Li, H., Luo, X.: A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme. IEEE-CAA J. Automatica Sin. 11(2), 487\u2013501 (2024). https:\/\/doi.org\/10.1109\/JAS.2023.124029","journal-title":"IEEE-CAA J. Automatica Sin."},{"key":"500_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.12051","volume":"229","author":"Y Chen","year":"2023","unstructured":"Chen, Y., Lin, M., He, Z., Polat, K., Alhudhaif, A., Alenezi, F.: Consistency-and dependence-guided knowledge distillation for object detection in remote sensing images. Expert Syst. Appl. 229, 120519 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.12051","journal-title":"Expert Syst. Appl."},{"key":"500_CR22","doi-asserted-by":"publisher","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian Error Linear Units (GELUs). arXiv preprint arXiv:1606.08415 (2016).https:\/\/doi.org\/10.48550\/arXiv.1606.08415","DOI":"10.48550\/arXiv.1606.08415"},{"key":"500_CR23","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vis."},{"key":"500_CR24","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.: LaSOT: A high-quality benchmark for large-scale single object tracking. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, Long Beach, CA, USA, June15\u201320, pp. 5374\u20135383 (2018). https:\/\/doi.org\/10.1109\/CVPR.2019.00552","DOI":"10.1109\/CVPR.2019.00552"},{"key":"500_CR25","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TPAMI.2019.2957464","volume":"43","author":"L Huang","year":"2022","unstructured":"Huang, L., Zhao, X., Huang, K.: GOT-10k: a Large high-diversity benchmark for generic object tracking in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 43, 1562\u20131577 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2019.2957464","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"500_CR26","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In :Proceedings of the European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, September 6\u201312,2014, pp.740\u2013755 (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"9","key":"500_CR27","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.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834\u20131848 (2015). https:\/\/doi.org\/10.1109\/TPAMI.2014.2388226","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"500_CR28","doi-asserted-by":"publisher","unstructured":"Li, S., Yeung, D.Y.: Visual object tracking for unmanned aerial vehicles: a benchmark and new motion models. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1\u20137 (2017). https:\/\/doi.org\/10.1609\/aaai.v31i1.11205","DOI":"10.1609\/aaai.v31i1.11205"},{"key":"500_CR29","doi-asserted-by":"publisher","unstructured":"Mueller, M., Smith, N., Ghanem, B.: A Benchmark and Simulator for UAV Tracking. In: Proceedings of the European Conference on Computer Vision, pp. 445\u2013461 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_27","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"500_CR30","doi-asserted-by":"publisher","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F.S., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 11\u201318, pp. 4310\u20134318 (2015).https:\/\/doi.org\/10.1109\/ICCV.2015.490","DOI":"10.1109\/ICCV.2015.490"},{"key":"500_CR31","doi-asserted-by":"publisher","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.: Staple: Complementary learners for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 26\u2013July 1, 2016, pp. 1401\u20131409 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.156","DOI":"10.1109\/CVPR.2016.156"},{"key":"500_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, J., Ma, S., Sclaroff, S.: MEEM: Robust tracking via multiple experts using entropy minimization. In: European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, September 6\u201312, 2014, pp. 188\u2013203 (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_13","DOI":"10.1007\/978-3-319-10599-4_13"},{"key":"500_CR33","doi-asserted-by":"publisher","unstructured":"Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326, 2017, pp. 2805\u20132813 (2017). https:\/\/doi.org\/10.48550\/arXiv.1704.06036","DOI":"10.48550\/arXiv.1704.06036"},{"key":"500_CR34","doi-asserted-by":"publisher","unstructured":"Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D.V., Tao, D.: MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 07\u201312, 2015, pp. 749\u2013758 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298675","DOI":"10.1109\/CVPR.2015.7298675"},{"key":"500_CR35","unstructured":"Danelljan, M., Hager, G., Khan, F.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, September 1\u20135, pp. 1\u201311 (2015). https:\/\/www.bmva.org\/bmvc\/2014\/papers\/paper038\/index.html"},{"key":"500_CR36","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1109\/TPAMI.2015.2509974","volume":"38","author":"S Hare","year":"2016","unstructured":"Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M., Hicks, S.L., Torr, P.H.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38, 2096\u20132109 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2015.2509974","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"500_CR37","doi-asserted-by":"publisher","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: a unifying approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June16\u201320, 2019, pp. 1328\u20131338 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00142","DOI":"10.1109\/CVPR.2019.00142"},{"key":"500_CR38","doi-asserted-by":"publisher","unstructured":"Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C.: Vital: Visual tracking via adversarial learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR 2018, Salt Lake City, UT, USA, June18\u201323, 2018, pp. 8990\u20138999 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00937","DOI":"10.1109\/CVPR.2018.00937"},{"key":"500_CR39","doi-asserted-by":"publisher","unstructured":"Yan, B., Zhao, H., Wang, D., Lu, H., Yang, X.: 'Skimming-Perusal' tracking: a framework for real-time and robust long-term tracking. In: IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27\u2013November 02, 2019, pp. 2385\u20132393 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00247","DOI":"10.1109\/ICCV.2019.00247"},{"key":"500_CR40","doi-asserted-by":"publisher","unstructured":"Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June27\u201330, pp. 4293\u20134302 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.465","DOI":"10.1109\/CVPR.2016.465"},{"key":"500_CR41","doi-asserted-by":"publisher","unstructured":"Luke\u017ei\u010d, A., Matas, J., Kristan, M.: D3S-A discriminative single shot segmentation tracker. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, Seattle, WA, USA, June 13\u201319, 2019, pp. 7131\u20137140 (2019). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00716","DOI":"10.1109\/CVPR42600.2020.00716"},{"key":"500_CR42","doi-asserted-by":"publisher","unstructured":"Wang, G., Luo, C., Xiong, Z., Zeng, W.: Spm-tracker: Series-parallel matching for real-time visual object tracking. In :Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2016,Long Beach, CA, USA, June 16\u201320, 2016, pp. 3643\u20133652 (2016). https:\/\/doi.org\/10.1109\/CVPR.2019.00376","DOI":"10.1109\/CVPR.2019.00376"},{"key":"500_CR43","doi-asserted-by":"publisher","unstructured":"Held, D., Thrun, S., Savarese, S.: Learning to track at 100 fps with deep regression networks. In: European Conference Computer Vision, ECCV 2016, Amsterdam, The Netherlands, October 11\u201314, pp. 749\u2013765 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_45","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"500_CR44","doi-asserted-by":"publisher","unstructured":"Danelljan, M., Robinson, A., Khan, F.S. Felsberg, M.: Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, pp. 472\u2013488 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_29","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"500_CR45","doi-asserted-by":"publisher","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Eco: Efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,CVPR2017, Honolulu, HI, USA, July21\u201326, 2017, pp. 6638\u20136646 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.733","DOI":"10.1109\/CVPR.2017.733"},{"key":"500_CR46","doi-asserted-by":"publisher","unstructured":"Sauer, A., Aljalbout, E., Haddadin, S.: Tracking holistic object representations. In: Proceedings of the 30th British Machine Vision Conference (BMVC), Cardiff, UK, September 9\u201312, 2019. arXiv preprint arXiv:1907.12920 (2019). https:\/\/doi.org\/10.48550\/arXiv.1907.12920","DOI":"10.48550\/arXiv.1907.12920"},{"key":"500_CR47","doi-asserted-by":"publisher","unstructured":"Galoogahi, H. K., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV 2017, Santiago, Chile, December 07\u201313, 2017, pp. 1144\u20131152 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.129","DOI":"10.1109\/ICCV.2017.129"},{"issue":"8","key":"500_CR48","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","volume":"39","author":"M Danelljan","year":"2017","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561\u20131575 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2609928","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"500_CR49","doi-asserted-by":"publisher","unstructured":"Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 07\u201313, 2015, pp. 3074\u20133082 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.352","DOI":"10.1109\/ICCV.2015.352"},{"key":"500_CR50","doi-asserted-by":"publisher","unstructured":"Wang, N., Zhou, W., Tian, Q., Hong, R., Meng, W., Li, H.: Multi-cue correlation filters for robust visual tracking. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18\u201323, 2018, pp. 4844\u20134853 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00509","DOI":"10.1109\/CVPR.2018.00509"},{"key":"500_CR51","doi-asserted-by":"publisher","unstructured":"Li, Y., Fu, C., Ding, F., Huang, Z., Lu, G.: AutoTrack: towards high-performance visual tracking for UAV with automatic spatio-temporal regularization. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 2020, June 13\u201319, pp. 11920\u201311929 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01194","DOI":"10.1109\/CVPR42600.2020.01194"},{"key":"500_CR52","doi-asserted-by":"publisher","unstructured":"Huang, Z., Fu, C., Li, Y., Lin, F., Lu, P.: Learning aberrance repressed correlation filters for real-time UAV tracking. In: IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27\u2013November 02, pp. 2891\u20132900 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00298","DOI":"10.1109\/ICCV.2019.00298"},{"key":"500_CR53","doi-asserted-by":"publisher","unstructured":"Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware Siamese networks for visual object tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds) Computer Vision \u2013 ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11213. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-01240-3_7","DOI":"10.1007\/978-3-030-01240-3_7"},{"key":"500_CR54","doi-asserted-by":"publisher","unstructured":"Li, X., Ma, C., Wu, B., He, Z., Yang, M.: Target-aware deep tracking. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 15\u201320, 2019, pp. 1369\u20131378 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00146","DOI":"10.1109\/CVPR.2019.00146"},{"key":"500_CR55","doi-asserted-by":"publisher","unstructured":"Li, F., Yao, Y., Li, P., Zhang, D., Zuo, W., Yang, M.H.: Integrating boundary and center correlation filters for visual tracking with aspect ratio variation. In: IEEE International Conference on Computer Vision Workshops, ICCVW 2017, Venice, Italy, October 22\u201329, 2017, pp. 2001\u20132009 (2017). https:\/\/doi.org\/10.1109\/ICCVW.2017.234","DOI":"10.1109\/ICCVW.2017.234"},{"key":"500_CR56","doi-asserted-by":"publisher","unstructured":"Zhang, T., Xu, C., Yang, M.H.: Multi-task correlation particle filter for robust object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326, 2017, pp. 4335\u20134343 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.512","DOI":"10.1109\/CVPR.2017.512"},{"key":"500_CR57","doi-asserted-by":"publisher","unstructured":"Wang, N., Song, Y., Ma, C., Zhou, W., Liu, W., Li, H.: Unsupervised deep tracking. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 15\u201320, 2019, pp. 1308\u20131317 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00140","DOI":"10.1109\/CVPR.2019.00140"},{"key":"500_CR58","doi-asserted-by":"publisher","unstructured":"Li, F., Tian, C., Zuo, W., Zhang, L., Yang, M.H.: Learning spatial-temporal regularized correlation filters for visual tracking. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18\u201323, 2018, pp. 4904\u20134913 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00515","DOI":"10.1109\/CVPR.2018.00515"},{"key":"500_CR59","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.patcog.2017.04.004","volume":"69","author":"L Zhang","year":"2017","unstructured":"Zhang, L., Suganthan, P.N.: Robust visual tracking via co-trained kernelized correlation filters. Pat. Rec. 69, 82\u201393 (2017). https:\/\/doi.org\/10.1016\/j.patcog.2017.04.004","journal-title":"Pat. Rec."},{"key":"500_CR60","doi-asserted-by":"publisher","unstructured":"Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic siamese network for visual object tracking. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22\u201329, 2017, pp. 1781\u20131789 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.196","DOI":"10.1109\/ICCV.2017.196"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00500-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00500-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00500-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T10:03:32Z","timestamp":1714385012000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00500-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["500"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00500-0","relation":{},"ISSN":["1875-6883"],"issn-type":[{"type":"electronic","value":"1875-6883"}],"subject":[],"published":{"date-parts":[[2024,4,29]]},"assertion":[{"value":"17 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"107"}}