{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T03:07:33Z","timestamp":1786590453502,"version":"3.56.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"8-9","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"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":["SIViP"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11760-024-03328-5","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T02:01:33Z","timestamp":1718589693000},"page":"6443-6454","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep feature based correlation filter for single object tracking in satellite videos"],"prefix":"10.1007","volume":"18","author":[{"given":"Devendra","family":"Sharma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajeev","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"issue":"3","key":"3328_CR1","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2014","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583\u2013596 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"3328_CR2","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/TGRS.2019.2943366","volume":"58","author":"S Xuan","year":"2019","unstructured":"Xuan, S., Li, S., Han, M., Wan, X., Xia, G.-S.: Object tracking in satellite videos by improved correlation filters with motion estimations. IEEE Trans. Geosci. Remote Sens. 58(2), 1074\u20131086 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3328_CR3","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhu, J., et\u00a0al.: A scale adaptive kernel correlation filter tracker with feature integration. In: ECCV workshops (2), 8926, pp. 254\u2013265. Citeseer, (2014)","DOI":"10.1007\/978-3-319-16181-5_18"},{"key":"3328_CR4","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, September 1-5, 2014. Bmva Press, (2014)","DOI":"10.5244\/C.28.65"},{"key":"3328_CR5","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp. 4310\u20134318, (2015)","DOI":"10.1109\/ICCV.2015.490"},{"key":"3328_CR6","doi-asserted-by":"crossref","unstructured":"Kiani Galoogahi, H., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp. 1135\u20131143. (2017)","DOI":"10.1109\/ICCV.2017.129"},{"key":"3328_CR7","doi-asserted-by":"crossref","unstructured":"Dai, K., Wang, D., Lu, H., Sun, C., Li, J.: Visual tracking via adaptive spatially-regularized correlation filters. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4670\u20134679. (2019)","DOI":"10.1109\/CVPR.2019.00480"},{"key":"3328_CR8","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: Eco: Efficient convolution operators for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6638\u20136646. (2017)","DOI":"10.1109\/CVPR.2017.733"},{"issue":"2","key":"3328_CR9","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1109\/TPAMI.2018.2797062","volume":"41","author":"T Zhang","year":"2018","unstructured":"Zhang, T., Changsheng, X., Yang, M.-H.: Learning multi-task correlation particle filters for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 365\u2013378 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3328_CR10","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Van Gool, L., Timofte, R.: Probabilistic regression for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 7183\u20137192. (2020)","DOI":"10.1109\/CVPR42600.2020.00721"},{"key":"3328_CR11","doi-asserted-by":"crossref","unstructured":"Yinda, Xu., Wang, Zeyu, Li, Zuoxin, Yuan, Ye., Gang, Yu.: Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines. In: Proceedings of the AAAI conference on artificial intelligence 34, pp. 12549\u201312556. (2020)","DOI":"10.1609\/aaai.v34i07.6944"},{"key":"3328_CR12","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: 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. (2019)","DOI":"10.1109\/CVPR.2019.00441"},{"key":"3328_CR13","doi-asserted-by":"crossref","unstructured":"Yan, B., Peng, H., Fu, J., Wang, D., Lu, H.: Learning spatio-temporal transformer for visual tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 10448\u201310457. (2021)","DOI":"10.1109\/ICCV48922.2021.01028"},{"key":"3328_CR14","doi-asserted-by":"crossref","unstructured":"Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., Lu, H.: Transformer tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8126\u20138135, (2021)","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"3328_CR15","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.patrec.2014.03.025","volume":"49","author":"T Vojir","year":"2014","unstructured":"Vojir, T., Noskova, J., Matas, J.: Robust scale-adaptive mean-shift for tracking. Pattern Recognit. Lett. 49, 250\u2013258 (2014)","journal-title":"Pattern Recognit. Lett."},{"issue":"1\u20133","key":"3328_CR16","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","volume":"77","author":"D Ross","year":"2008","unstructured":"Ross, D., Lin, R.S., Lim, J., Lim, J., et al.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77(1\u20133), 125\u2013141 (2008)","journal-title":"Int. J. Comput. Vision"},{"key":"3328_CR17","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: A benchmark. In: Proceedings of the IEEE conference on computer vision and patter recognition, pp. 2411\u20132418, (2013)","DOI":"10.1109\/CVPR.2013.312"},{"key":"3328_CR18","doi-asserted-by":"crossref","unstructured":"Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In: 2012 IEEE Conference on Computer vision and pattern recognition, pp. 1838\u20131845. IEEE, (2012)","DOI":"10.1109\/CVPR.2012.6247882"},{"key":"3328_CR19","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp. 2544\u20132550. IEEE, (2010)","DOI":"10.1109\/CVPR.2010.5539960"},{"issue":"8","key":"3328_CR20","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","volume":"39","author":"M Danelljan","year":"2016","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 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3328_CR21","first-page":"131","volume":"190","author":"R Rifkin","year":"2003","unstructured":"Rifkin, R., Yeo, G., Poggio, T., et al.: Regularized least-squares classification. Nato Sci. Ser. Sub Ser. III Comput. Syst. Sci. 190, 131\u2013154 (2003)","journal-title":"Nato Sci. Ser. Sub Ser. III Comput. Syst. Sci."},{"key":"3328_CR22","doi-asserted-by":"crossref","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Computer Vision\u2013ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV 12, pp. 702\u2013715. Springer, (2012)","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"3328_CR23","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Shahbaz Khan, F., Felsberg, M., Van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1090\u20131097. (2014)","DOI":"10.1109\/CVPR.2014.143"},{"issue":"9","key":"3328_CR24","first-page":"1135","volume":"46","author":"W Jiaqi","year":"2017","unstructured":"Jiaqi, W., Guo, Z., Taoyang, W., Yonghua, J.: Satellite video point-target tracking in combination with motion smoothness constraint and grayscale feature. Acta Geod. et Cartogr. Sin. 46(9), 1135 (2017)","journal-title":"Acta Geod. et Cartogr. Sin."},{"issue":"8","key":"3328_CR25","doi-asserted-by":"publisher","first-page":"3043","DOI":"10.1109\/JSTARS.2019.2917703","volume":"12","author":"D Bo","year":"2019","unstructured":"Bo, D., Cai, S., Chen, W.: Object tracking in satellite videos based on a multiframe optical flow tracker. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12(8), 3043\u20133055 (2019)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"2","key":"3328_CR26","first-page":"168","volume":"15","author":"D Bo","year":"2017","unstructured":"Bo, D., Sun, Y., Cai, S., Chen, W., Qian, D.: Object tracking in satellite videos by fusing the kernel correlation filter and the three-frame-difference algorithm. IEEE Geosci. Remote Sens. Lett. 15(2), 168\u2013172 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"11","key":"3328_CR27","doi-asserted-by":"publisher","first-page":"8719","DOI":"10.1109\/TGRS.2019.2922648","volume":"57","author":"J Shao","year":"2019","unstructured":"Shao, J., Bo, D., Chen, W., Zhang, L.: Can we track targets from space? a hybrid kernel correlation filter tracker for satellite video. IEEE Trans. Geosci. Remote Sen. 57(11), 8719\u20138731 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"issue":"9","key":"3328_CR28","doi-asserted-by":"publisher","first-page":"3538","DOI":"10.1109\/JSTARS.2019.2933488","volume":"12","author":"Y Guo","year":"2019","unstructured":"Guo, Y., Yang, D., Chen, Z.: Object tracking on satellite videos: A correlation filter-based tracking method with trajectory correction by kalman filter. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sen. 12(9), 3538\u20133551 (2019)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sen."},{"issue":"3","key":"3328_CR29","first-page":"55","volume":"39","author":"J-Q WU","year":"2019","unstructured":"WU, J.-Q., WANG, T.-Y., YAN, J., ZHANG, G., JIANG, X.-H., WANG, Y.-M., BAI, Q., YUAN, C.: Satellite video point- target tracking based on hu correlation filter. Chin. Sp. Sci. Technol. 39(3), 55 (2019)","journal-title":"Chin. Sp. Sci. Technol."},{"issue":"10","key":"3328_CR30","doi-asserted-by":"publisher","first-page":"7010","DOI":"10.1109\/TGRS.2020.2978512","volume":"58","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Wang, T., Zhang, G., Cheng, Q., Jia-qi, W.: Small target tracking in satellite videos using background compensation. IEEE Trans. Geosci. Remote Sen. 58(10), 7010\u20137021 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"key":"3328_CR31","doi-asserted-by":"crossref","unstructured":"Zhaopeng, H., Yang, D., Zhang, K., Chen, Z.: Object tracking in satellite videos based on convolutional regression network with appearance and motion features. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sen. 13, 783\u2013793 (2020)","DOI":"10.1109\/JSTARS.2020.2971657"},{"key":"3328_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040221","volume":"60","author":"W Zhang","year":"2021","unstructured":"Zhang, W., Jiao, L., Liu, F., Li, L., Liu, X., Liu, J.: Mblt: Learning motion and background for vehicle tracking in satellite videos. IEEE Trans. Geosci. Remote Sen. 60, 1\u201315 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"issue":"10","key":"3328_CR33","doi-asserted-by":"publisher","first-page":"7860","DOI":"10.1109\/TGRS.2019.2916953","volume":"57","author":"J Shao","year":"2019","unstructured":"Shao, J., Bo, D., Chen, W., Zhang, L.: Tracking objects from satellite videos: A velocity feature based correlation filter. IEEE Trans. Geosci. Remote Sen. 57(10), 7860\u20137871 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"key":"3328_CR34","first-page":"1","volume":"60","author":"Y Cui","year":"2021","unstructured":"Cui, Y., Hou, B., Qian, W., Ren, B., Wang, S., Jiao, L.: Remote sensing object tracking with deep reinforcement learning under occlusion. IEEE Trans. Geosci. Remote Sen. 60, 1\u201313 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"key":"3328_CR35","doi-asserted-by":"crossref","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems. (1960)","DOI":"10.1115\/1.3662552"},{"key":"3328_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/3189691","volume":"2020","author":"N-D Nguyen","year":"2020","unstructured":"Nguyen, N.-D., Do, T., Ngo, T.D., Le, D.-D.: An evaluation of deep learning methods for small object detection. J. Electr. Comput. Eng. 2020, 1\u201318 (2020)","journal-title":"J. Electr. Comput. Eng."},{"key":"3328_CR37","doi-asserted-by":"crossref","unstructured":"Ning, J., Guan, H., Spratling, M.: Rethinking the backbone architecture for tiny object detection. arXiv preprint arXiv:2303.11267, (2023)","DOI":"10.5220\/0011643500003417"},{"key":"3328_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3230378","volume":"60","author":"M Zhao","year":"2022","unstructured":"Zhao, M., Li, S., Xuan, S., Kou, L., Gong, S., Zhou, Z.: Satsot: A benchmark dataset for satellite video single object tracking. IEEE Trans. Geosci. Remote Sen. 60, 1\u201311 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"key":"3328_CR39","first-page":"1","volume":"60","author":"Y Li","year":"2022","unstructured":"Li, Y., Bian, C., Chen, H.: Object tracking in satellite videos: correlation particle filter tracking method with motion estimation by kalman filter. IEEE Trans. Geosci. Remote Sen. 60, 1\u201312 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"key":"3328_CR40","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Atom: Accurate tracking by overlap maximization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 4660\u20134669, (2019)","DOI":"10.1109\/CVPR.2019.00479"},{"key":"3328_CR41","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A.N., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Computer Vision\u2013ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14, pp. 850\u2013865. Springer, (2016)","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"3328_CR42","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8971\u20138980, (2018)","DOI":"10.1109\/CVPR.2018.00935"},{"key":"3328_CR43","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.S.: Fast online object tracking and segmentation: A unifying approach. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition, pp. 1328\u20131338, (2019)","DOI":"10.1109\/CVPR.2019.00142"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03328-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03328-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03328-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T23:39:24Z","timestamp":1732232364000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03328-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,17]]},"references-count":43,"journal-issue":{"issue":"8-9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3328"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03328-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,17]]},"assertion":[{"value":"11 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2024","order":4,"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":"In this work, no human or animal was directly involved. The datasets used in this study are publicly available online. Hence, ethical approval was not required for this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}