{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T18:51:32Z","timestamp":1726253492697},"reference-count":56,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"In recent years, discriminative correlation filter (DCF)-based trackers have made considerable progress and drawn widespread attention in the unmanned aerial vehicle (UAV) tracking community. Most existing trackers collect historical information, e.g., training samples, previous filters, and response maps, to promote their discrimination and robustness. Under UAV-specific tracking challenges, e.g., fast motion and view change, variations of both the target and its environment in the new frame are unpredictable. Interfered by future unknown environments, trackers that trained with historical information may be confused by the new context, resulting in tracking failure. In this paper, we propose a novel future-aware correlation filter tracker, i.e., FACF. The proposed method aims at effectively utilizing context information in the new frame for better discriminative and robust abilities, which consists of two stages: future state awareness and future context awareness. In the former stage, an effective time series forecast method is employed to reason a coarse position of the target, which is the reference for obtaining a context patch in the new frame. In the latter stage, we firstly obtain the single context patch with an efficient target-aware method. Then, we train a filter with the future context information in order to perform robust tracking. Extensive experimental results obtained from three UAV benchmarks, i.e., UAV123_10fps, DTB70, and UAVTrack112, demonstrate the effectiveness and robustness of the proposed tracker. Our tracker has comparable performance with other state-of-the-art trackers while running at \u223c49 FPS on a single CPU.<\/jats:p>","DOI":"10.3390\/rs13204111","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T23:02:16Z","timestamp":1634252536000},"page":"4111","source":"Crossref","is-referenced-by-count":11,"title":["Learning Future-Aware Correlation Filters for Efficient UAV Tracking"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7307-0697","authenticated-orcid":false,"given":"Fei","family":"Zhang","sequence":"first","affiliation":[{"name":"Aeronautics Engineering College, Air Force Engineering University, Xi\u2019an 710038, China"}]},{"given":"Shiping","family":"Ma","sequence":"additional","affiliation":[{"name":"Aeronautics Engineering College, Air Force Engineering University, Xi\u2019an 710038, China"}]},{"given":"Lixin","family":"Yu","sequence":"additional","affiliation":[{"name":"Air Traffic Control and Navigation College, Air Force Engineering University, Xi\u2019an 710051, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7328-4815","authenticated-orcid":false,"given":"Yule","family":"Zhang","sequence":"additional","affiliation":[{"name":"Air and Missile Defense College, Air Force Engineering University, Xi\u2019an 710051, China"}]},{"given":"Zhuling","family":"Qiu","sequence":"additional","affiliation":[{"name":"Aeronautics Engineering College, Air Force Engineering University, Xi\u2019an 710038, China"}]},{"given":"Zhenyu","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbing 150080, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Miao, Y., Li, J., Bao, Y., Liu, F., and Hu, C. (2021). Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR. Remote Sens., 13.","key":"ref_1","DOI":"10.3390\/rs13173429"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1109\/TMM.2020.2997193","article-title":"Image-Only Real-Time Incremental UAV Image Mosaic for Multi-Strip Flight","volume":"23","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Multimed."},{"doi-asserted-by":"crossref","unstructured":"Mcarthur, D.R., Chowdhury, A.B., and Cappelleri, D.J. (2018, January 21\u201325). Autonomous Control of the Interacting-BoomCopter UAV for Remote Sensor Mounting. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","key":"ref_3","DOI":"10.1109\/ICRA.2018.8461119"},{"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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","key":"ref_4","DOI":"10.1109\/CVPR.2010.5539960"},{"doi-asserted-by":"crossref","unstructured":"Li, F., Tian, C., Zuo, W., Zhang, L., and Yang, M.H. (2018, January 18\u201322). Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","key":"ref_5","DOI":"10.1109\/CVPR.2018.00515"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F., and Felsberg, M. (2014, January 1\u20135). Accurate scale estimation for robust visual tracking. Proceedings of the British Machine Vision Conference (BMVC), Nottingham, UK.","key":"ref_6","DOI":"10.5244\/C.28.65"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Shahbaz Khan, F., and Felsberg, M. (2017, January 21\u201326). Eco: Efficient convolution operators for tracking. Proceedings of the IEEE Conference on Computer Cision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_7","DOI":"10.1109\/CVPR.2017.733"},{"doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (2016, January 8\u201316). Fully-convolutional siamese networks for object tracking. Proceedings of the European Conference on Computer Cision (ECCVW), Amsterdam, The Netherlands.","key":"ref_8","DOI":"10.1007\/978-3-319-48881-3_56"},{"doi-asserted-by":"crossref","unstructured":"Fu, C., Lin, F., Li, Y., and Chen, G. (2019). Correlation Filter-Based Visual Tracking for UAV with Online Multi-Feature Learning. Remote Sens., 11.","key":"ref_9","DOI":"10.3390\/rs11050549"},{"unstructured":"Fu, C., Li, B., Ding, F., Lin, F., and Lu, G. (2020). Correlation Filter for UAV-Based Aerial Tracking: A Review and Experimental Evaluation. arXiv.","key":"ref_10"},{"key":"ref_11","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":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Shahbaz Khan, F., and Felsberg, M. (2015, January 7\u201313). Learning spatially regularized correlation filters for visual tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","key":"ref_12","DOI":"10.1109\/ICCV.2015.490"},{"doi-asserted-by":"crossref","unstructured":"Kiani Galoogahi, H., Fagg, A., and Lucey, S. (2017, January 22\u201329). Learning background-aware correlation filters for visual tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","key":"ref_13","DOI":"10.1109\/ICCV.2017.129"},{"doi-asserted-by":"crossref","unstructured":"Lukezic, A., Vojir, T., \u010cehovin Zajc, L., Matas, J., and Kristan, M. (2017, January 21\u201326). Discriminative correlation filter with channel and spatial reliability. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_14","DOI":"10.1109\/CVPR.2017.515"},{"doi-asserted-by":"crossref","unstructured":"Dai, K., Wang, D., Lu, H., Sun, C., and Li, J. (2019, January 16\u201320). Visual Tracking via Adaptive Spatially-Regularized Correlation Filters. Proceedings of the IEEE Conference on Computer Cision and Pattern Recognition (CVPR), Long Beach, CA, USA.","key":"ref_15","DOI":"10.1109\/CVPR.2019.00480"},{"doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2017, January 21\u201326). Context-aware correlation filter tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_16","DOI":"10.1109\/CVPR.2017.152"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sigpro.2019.107324","article-title":"Surrounding-Aware Correlation Filter for UAV Tracking with Selective Spatial Regularization","volume":"167","author":"Fu","year":"2020","journal-title":"Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1109\/TMM.2020.2990064","article-title":"Intermittent Contextual Learning for Keyfilter-Aware UAV Object Tracking Using Deep Convolutional Feature","volume":"23","author":"Li","year":"2021","journal-title":"IEEE Trans. Multimed."},{"unstructured":"Xu, T., Feng, Z.H., Wu, X.J., and Kittler, J. (November, January 27). Joint group feature selection and discriminative filter learning for robust visual object tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea.","key":"ref_19"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8107","DOI":"10.1109\/TIP.2020.2981813","article-title":"Mining Spatial-Temporal Similarity for Visual Tracking","volume":"29","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"unstructured":"Huang, Z., Fu, C., Li, Y., Lin, F., and Lu, P. (November, January 27). Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea.","key":"ref_21"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6301","DOI":"10.1109\/TGRS.2020.3030265","article-title":"Disruptor-Aware Interval-Based Response Inconsistency for Correlation Filters in Real-Time Aerial Tracking","volume":"59","author":"Fu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Li, Y., Fu, C., Ding, F., Huang, Z., and Lu, G. (2020, January 13\u201319). AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","key":"ref_23","DOI":"10.1109\/CVPR42600.2020.01194"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"276","DOI":"10.14419\/gjma.v2i4.3253","article-title":"A comparison between single exponential smoothing (SES), double exponential smoothing (DES), holt\u2019s (brown) and adaptive response rate exponential smoothing (ARRES) techniques in forecasting Malaysia population","volume":"2","author":"Nazim","year":"2014","journal-title":"Glob. J. Math. Anal."},{"doi-asserted-by":"crossref","unstructured":"Li, S., and Yeung, D.Y. (2017, January 4\u20139). Visual object tracking for unmanned aerial vehicles: A benchmark and new motion models. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","key":"ref_25","DOI":"10.1609\/aaai.v31i1.11205"},{"doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2016, January 11\u201314). A benchmark and simulator for uav tracking. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","key":"ref_26","DOI":"10.1007\/978-3-319-46448-0_27"},{"doi-asserted-by":"crossref","unstructured":"Fu, C., Cao, Z., Li, Y., Ye, J., and Feng, C. (2021). Onboard Real-Time Aerial Tracking with Efficient Siamese Anchor Proposal Network. IEEE Trans. Geosci. Remote Sens., 1\u201313.","key":"ref_27","DOI":"10.1109\/TGRS.2021.3083880"},{"doi-asserted-by":"crossref","unstructured":"Li, Y., and Zhu, J. (2014, January 6\u201312). A scale adaptive kernel correlation filter tracker with feature integration. Proceedings of the European Conference on Computer Vision Workshops (ECCV), Zurich, Switzerland.","key":"ref_28","DOI":"10.1007\/978-3-319-16181-5_18"},{"unstructured":"Wang, C., Zhang, L., Xie, L., and Yuan, J. (2017, January 2\u20137). Kernel cross-correlator. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","key":"ref_29"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Shahbaz Khan, F., and Felsberg, M. (2016, January 27\u201330). Adaptive decontamination of the training set: A unified formulation for discriminative visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_30","DOI":"10.1109\/CVPR.2016.159"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., Felsberg, M., and Van De Weijer, J. (2014, January 23\u201328). Adaptive Color Attributes for Real-Time Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","key":"ref_31","DOI":"10.1109\/CVPR.2014.143"},{"doi-asserted-by":"crossref","unstructured":"Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., and Yang, M.H. (2016, January 27\u201330). Hedged Deep Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_32","DOI":"10.1109\/CVPR.2016.466"},{"doi-asserted-by":"crossref","unstructured":"Danelljan, M., Robinson, A., Khan, F.S., and Felsberg, M. (2016, January 11\u201314). Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","key":"ref_33","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5596","DOI":"10.1109\/TIP.2019.2919201","article-title":"Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking","volume":"28","author":"Xu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Zhu, X.F., Wu, X.J., Xu, T., Feng, Z., and Kittler, J. (2021). Robust Visual Object Tracking via Adaptive Attribute-Aware Discriminative Correlation Filters. IEEE Trans. Multimed., 1.","key":"ref_35","DOI":"10.1109\/TMM.2021.3050073"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.neucom.2021.01.057","article-title":"Learning spatio-temporal correlation filter for visual tracking","volume":"436","author":"Yan","year":"2021","journal-title":"Neurocomputing"},{"doi-asserted-by":"crossref","unstructured":"Wang, N., Zhou, W., Tian, Q., Hong, R., Wang, M., and Li, H. (2018, January 18\u201322). Multi-cue correlation filters for robust visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","key":"ref_37","DOI":"10.1109\/CVPR.2018.00509"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8940","DOI":"10.1109\/TGRS.2020.2992301","article-title":"Object Saliency-Aware Dual Regularized Correlation Filter for Real-Time Aerial Tracking","volume":"58","author":"Fu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104116","DOI":"10.1016\/j.engappai.2020.104116","article-title":"Learning dynamic regression with automatic distractor repression for real-time UAV tracking","volume":"98","author":"Fu","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"unstructured":"Zheng, G., Fu, C., Ye, J., Lin, F., and Ding, F. (June, January 30). Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label. Proceedings of the International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","key":"ref_40"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2160","DOI":"10.1109\/TCSVT.2020.3023440","article-title":"Learning Temporary Block-Based Bidirectional Incongruity-Aware Correlation Filters for Efficient UAV Object Tracking","volume":"31","author":"Lin","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"doi-asserted-by":"crossref","unstructured":"Xue, X., Li, Y., and Shen, Q. (2018). Unmanned aerial vehicle object tracking by correlation filter with adaptive appearance model. Sensors, 18.","key":"ref_42","DOI":"10.3390\/s18092751"},{"doi-asserted-by":"crossref","unstructured":"Zha, Y., Wu, M., Qiu, Z., Sun, J., Zhang, P., and Huang, W. (2020). Online Semantic Subspace Learning with Siamese Network for UAV Tracking. Remote Sens., 12.","key":"ref_43","DOI":"10.3390\/rs12020325"},{"doi-asserted-by":"crossref","unstructured":"Zhuo, L., Liu, B., Zhang, H., Zhang, S., and Li, J. (2021). MultiRPN-DIDNet: Multiple RPNs and Distance-IoU Discriminative Network for Real-Time UAV Target Tracking. Remote Sens., 13.","key":"ref_44","DOI":"10.3390\/rs13142772"},{"unstructured":"Ren, S., He, K., Girshick, R.B., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the 29th International Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada.","key":"ref_45"},{"doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R.B. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, (ICCV), Venice, Italy.","key":"ref_46","DOI":"10.1109\/ICCV.2017.322"},{"unstructured":"Li, B., Fu, C., Ding, F., Ye, J., and Lin, F. (2021). All-Day Object Tracking for Unmanned Aerial Vehicle. arXiv.","key":"ref_47"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","volume":"3","author":"Boyd","year":"2011","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1214\/aoms\/1177729893","article-title":"Adjustment of an inverse matrix corresponding to a change in one element of a given matrix","volume":"21","author":"Sherman","year":"1950","journal-title":"Ann. Math. Stat."},{"doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H. (2016, January 27\u201330). Staple: Complementary learners for real-time tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_50","DOI":"10.1109\/CVPR.2016.156"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1007\/s11263-020-01357-4","article-title":"Unsupervised Deep Representation Learning for Real-Time Tracking","volume":"129","author":"Wang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6123","DOI":"10.1109\/TIP.2020.2989544","article-title":"Real-Time Correlation Tracking Via Joint Model Compression and Transfer","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Li, X., Ma, C., Wu, B., He, Z., and Yang, M.H. (2019, January 16\u201320). Target-aware deep tracking. Proceedings of the IEEE Conference on Computer Cision and Pattern Recognition (CVPR), Long Beach, CA, USA.","key":"ref_53","DOI":"10.1109\/CVPR.2019.00146"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.patcog.2017.04.004","article-title":"Robust Visual Tracking via Co-trained Kernelized Correlation Filters","volume":"69","author":"Zhang","year":"2017","journal-title":"Pattern Recognit."},{"doi-asserted-by":"crossref","unstructured":"Wang, N., Song, Y., Ma, C., Zhou, W., Liu, W., and Li, H. (2019, January 16\u201320). Unsupervised deep tracking. Proceedings of the IEEE Conference on Computer Cision and Pattern Recognition (CVPR), Long Beach, CA, USA.","key":"ref_55","DOI":"10.1109\/CVPR.2019.00140"},{"doi-asserted-by":"crossref","unstructured":"Valmadre, J., Bertinetto, L., Henriques, J.F., Vedaldi, A., and Torr, P.H.S. (2017, January 21\u201326). End-to-End Representation Learning for Correlation Filter Based Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, USA.","key":"ref_56","DOI":"10.1109\/CVPR.2017.531"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T19:55:35Z","timestamp":1725911735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":56,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204111"],"URL":"http:\/\/dx.doi.org\/10.3390\/rs13204111","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,10,14]]}}}
