{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:09:17Z","timestamp":1760058557516,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaanxi provincial fund","award":["2023\u2014YBGY\u2014234"],"award-info":[{"award-number":["2023\u2014YBGY\u2014234"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Video satellites provide high-temporal-resolution remote sensing images that enable continuous monitoring of the ground for applications such as target tracking and airport traffic detection. In this paper, we address the problems of object occlusion and the tracking of rotating objects in satellite videos by introducing a rotation-adaptive tracking algorithm for correlation filters with motion estimation (RACFME). Our algorithm proposes the following improvements over the KCF method: (a) A rotation-adaptive feature enhancement module (RA) is proposed to obtain the rotated image block by affine transformation combined with the target rotation direction prior, which overcomes the disadvantage of HOG features lacking rotation adaptability, improves tracking accuracy while ensuring real-time performance, and solves the problem of tracking failure due to insufficient valid positive samples when tracking rotating targets. (b) Based on the correlation between peak response and occlusion, an occlusion detection method for vehicles and ships in satellite video is proposed. (c) Motion estimations are achieved by combining Kalman filtering with motion trajectory averaging, which solves the problem of tracking failure in the case of object occlusion. The experimental results show that the proposed RACFME algorithm can track a moving target with a 95% success score, and the RA module and ME both play an effective role.<\/jats:p>","DOI":"10.3390\/sym17040608","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:48:46Z","timestamp":1744850926000},"page":"608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RACFME: Object Tracking in Satellite Videos by Rotation Adaptive Correlation Filters with Motion Estimations"],"prefix":"10.3390","volume":"17","author":[{"given":"Xiongzhi","family":"Wu","sequence":"first","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xi\u2019an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology, Xi\u2019an 710119, China"}]},{"given":"Haifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"Xi\u2019an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology, Xi\u2019an 710119, China"}]},{"given":"Chao","family":"Mei","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"Xi\u2019an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology, Xi\u2019an 710119, China"}]},{"given":"Jiaxin","family":"Wu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xi\u2019an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology, Xi\u2019an 710119, China"}]},{"given":"Han","family":"Ai","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"Xi\u2019an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology, Xi\u2019an 710119, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos","volume":"21","author":"Zhang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shangguan, D., Chen, L., and Ding, J. (2020). A Digital Twin-Based Approach for the Fault Diagnosis and Health Monitoring of a Complex Satellite System. Symmetry, 12.","DOI":"10.3390\/sym12081307"},{"key":"ref_3","first-page":"3640","article-title":"Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification","volume":"16","author":"Ghaban","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6508","DOI":"10.1109\/TIP.2024.3494600","article-title":"Pro2Diff: Proposal Propagation for Multi-Object Tracking via the Diffusion Model","volume":"33","author":"Liu","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1109\/TGRS.2019.2943366","article-title":"Object tracking in satellite videos by improved correlation filters with motion estimation","volume":"58","author":"Xuan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-speed tracking with kernelized correlation filters","volume":"37","author":"Henriques","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, H., and Man, Y. (2016, January 10\u201315). Moving ship detection based on visual saliency for video satellite. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729316"},{"key":"ref_9","first-page":"1135","article-title":"Satellite Video Point-target Tracking in Combination with Motion Smoothness Constraint and Grayscale Feature","volume":"46","author":"Wu","year":"2017","journal-title":"J. Geomat."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, C., Wang, C., Song, J., and Xu, Y. (2022). Based Satellite Video Object Tracking: A Review. Remote Sens., 14.","DOI":"10.3390\/rs14153674"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.isprsjprs.2024.03.013","article-title":"Satellite Video Single Object Tracking: A Systematic Review and An Oriented Object Tracking Benchmark","volume":"210","author":"Chen","year":"2024","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/LGRS.2017.2776899","article-title":"Object tracking in satellite videos by fusing the kernel correlation filter and the three-framedifference algorithm","volume":"15","author":"Du","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Possegger, H., Mauthner, T., and Bischof, H. (2015, January 7\u201312). In defense of color-based model-free tracking. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298823"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.cviu.2008.08.006","article-title":"Object tracking using sift features and mean shift","volume":"113","author":"Zhou","year":"2009","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/3-540-45783-6_43","article-title":"Object tracking with an adaptive color-based particle filter","volume":"Volume 2449","author":"Nummiaro","year":"2002","journal-title":"Pattern Recognition"},{"key":"ref_16","unstructured":"Grabner, H., and Bischof, H. (2006, January 17\u201322). On-line boosting and vision. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Grabner, H., Leistner, C., and Bischof, H. (2008). Semi-supervised on-line boosting for robust tracking. Computer Vision\u2014ECCV 2008, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-540-88682-2_19"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TPAMI.2015.2509974","article-title":"Struck: Structured output tracking with kernels","volume":"38","author":"Hare","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nam, H., and Han, B. (July, January 26). Learning multi-domain convolutional neural networks for visual tracking. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.465"},{"key":"ref_20","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, MIT Press."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Held, D., Thrun, S., and Savarese, S. (2016, January 8\u201316). Learning to track at 100 fps with deep regression networks. Proceedings of the Computer Vision ECCV, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"ref_22","first-page":"10758587","article-title":"Research on ECO-HC Target Tracking Algorithm Based on Adaptive Template Update and Multi-Feature Fusion","volume":"23","author":"Li","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., and Batista, J. (2012, January 7\u201313). Exploiting the circulant structure of tracking-by-detection with kernels. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kwon, J., and Lee, K. (2010, January 13\u201318). Visual Tracking Decomposition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539821"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103124","DOI":"10.1016\/j.media.2024.103124","article-title":"Cross-scale Multi-instance Learning for Pathological Image Diagnosis","volume":"82","author":"Deng","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Luiten, J., Torr, P.H., and Leibe, B. (2020, January 13\u201319). Siam R-CNN: Visual Tracking by Re-Detection. Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00661"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/608\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:15:58Z","timestamp":1760030158000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,16]]},"references-count":27,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040608"],"URL":"https:\/\/doi.org\/10.3390\/sym17040608","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,4,16]]}}}