{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:04:04Z","timestamp":1760231044775,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T00:00:00Z","timestamp":1660953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871460","2019GXNSFBA245056"],"award-info":[{"award-number":["61871460","2019GXNSFBA245056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Guangxi","award":["61871460","2019GXNSFBA245056"],"award-info":[{"award-number":["61871460","2019GXNSFBA245056"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently.<\/jats:p>","DOI":"10.3390\/rs14164073","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4073","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6360-5435","authenticated-orcid":false,"given":"Bin","family":"Lin","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"College of Science, Guilin University of Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6923-672X","authenticated-orcid":false,"given":"Yunpeng","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0182-1519","authenticated-orcid":false,"given":"Bendu","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7370-1754","authenticated-orcid":false,"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113711","DOI":"10.1016\/j.eswa.2020.113711","article-title":"Recent Trends in Multicue Based Visual Tracking: A Review","volume":"162","author":"Kumar","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","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 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00298"},{"key":"ref_3","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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01194"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fu, C., Ding, F., Li, Y., Jin, J., and Feng, C. (2020\u201324, January 24). DR2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341761"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Specht, M., Stateczny, A., Specht, C., Wid\u017agowski, S., Lewicka, O., and Wi\u015bniewska, M. (2021). Concept of an Innovative Autonomous Unmanned System for Bathymetric Monitoring of Shallow Waterbodies (INNOBAT System). Energies, 14.","DOI":"10.3390\/en14175370"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, D., Xing, S., He, Y., Yu, J., Xu, Q., and Li, P. (2022). Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection. Sensors, 22.","DOI":"10.3390\/s22041379"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Burdziakowski, P. (2020). Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12050810"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s10846-016-0464-7","article-title":"Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles","volume":"88","author":"Yuan","year":"2017","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nikolakopoulos, K.G., Lampropoulou, P., Fakiris, E., Sardelianos, D., and Papatheodorou, G. (2018). Synergistic Use of UAV and USV Data and Petrographic Analyses for the Investigation of Beachrock Formations: A Case Study from Syros Island, Aegean Sea, Greece. Minerals, 8.","DOI":"10.3390\/min8110534"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/MGRS.2021.3072992","article-title":"Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation","volume":"10","author":"Fu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_12","unstructured":"Jia, X., Lu, H., and Yang, M.H. (2012, January 16\u201321). Visual Tracking via Adaptive Structural Local Sparse Appearance Model. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sevilla-Lara, L., and Learned-Miller, E. (2012, January 16\u201321). Distribution Fields for Tracking. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247891"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, S., Yang, Q., Lau, R.W., Wang, J., and Yang, M.H. (2013, January 23\u201328). Visual Tracking via Locality Sensitive Histograms. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.314"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","article-title":"Robust Object Tracking with Online Multiple Instance Learning","volume":"33","author":"Babenko","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/TPAMI.2011.239","article-title":"Tracking-Learning-Detection","volume":"34","author":"Kalal","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization. ECCV 2014: Proceedings of the Computer Vision, Zurich, Switzerland, 6\u201312 September 2014, Springer International Publishing."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2096","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":"Ma, C., Huang, J.B., Yang, X., and Yang, M.H. (2015, January 7\u201313). Hierarchical Convolutional Features for Visual Tracking. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.352"},{"key":"ref_20","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."},{"key":"ref_21","unstructured":"Hua, G., and J\u00e9gou, H. (2016). Fully-Convolutional Siamese Networks for Object Tracking. ECCV 2016 Workshops: Proceedings of the Computer Vision, Amsterdam, The Netherlands, 8\u201316 October 2016, Springer International Publishing."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., and Hu, X. (2018, January 18\u201323). High Performance Visual Tracking with Siamese Region Proposal Network. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., and Torr, P.H. (2019, January 15\u201320). Fast Online Object Tracking and Segmentation: A Unifying Approach. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00142"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., and Lu, H. (2021, January 20\u201325). Transformer Tracking. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"ref_25","first-page":"1","article-title":"Onboard Real-Time Aerial Tracking with Efficient Siamese Anchor Proposal Network","volume":"60","author":"Fu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","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 Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_27","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_28","unstructured":"Agapito, L., Bronstein, M.M., and Rother, C. (2015). A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. ECCV 2014 Workshops: Proceedings of the Computer Vision, Zurich, Switzerland, 6\u201312 September 2014, Springer International Publishing."},{"key":"ref_29","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 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.143"},{"key":"ref_30","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.","DOI":"10.3390\/rs11050549"},{"key":"ref_31","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."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, F., Ma, S., Yu, L., Zhang, Y., Qiu, Z., and Li, Z. (2021). Learning Future-Aware Correlation Filters for Efficient UAV Tracking. Remote Sens., 13.","DOI":"10.3390\/rs13204111"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems\u2014NIPS\u201917, Long Beach, CA, USA."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Zhu, G., Wang, J., Wu, Y., Zhang, X., and Lu, H. (2016, January 7\u201312). MC-HOG Correlation Tracking with Saliency Proposal. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v30i1.10450"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhou, W., Tian, Q., Hong, R., Wang, M., and Li, H. (2018, January 18\u201323). Multi-cue Correlation Filters for Robust Visual Tracking. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00509"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, L., Xie, L., and Yuan, J. (2018, January 2\u20137). Kernel Cross-Correlator. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11710"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/TPAMI.2018.2797062","article-title":"Learning Multi-Task Correlation Particle Filters for Visual Tracking","volume":"41","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","unstructured":"Li, Y., Zhu, J., Hoi, S.C., Song, W., Wang, Z., and Liu, H. (2019). Robust Estimation of Similarity Transformation for Visual Object Tracking. AAAI\u201919\/IAAI\u201919\/EAAI\u201919: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Hawaii USA, 27 January\u20131 February 2019, AAAI Press."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Sim, T., and Lucey, S. (2015, January 7\u201312). Correlation filters with limited boundaries. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299094"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F.S., and Felsberg, M. (2015, January 7\u201313). Learning Spatially Regularized Correlation Filters for Visual Tracking. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.490"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Fagg, A., and Lucey, S. (2017, January 22\u201329). Learning Background-Aware Correlation Filters for Visual Tracking. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.129"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Luke\u017eic, A., Voj\u00edr, T., Zajc, L.C., Matas, J., and Kristan, M. (2017, January 21\u201326). Discriminative Correlation Filter with Channel and Spatial Reliability. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.515"},{"key":"ref_45","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). A Benchmark and Simulator for UAV Tracking. ECCV 2016: Proceedings of the Computer Vision, Amsterdam, The Netherlands, 8\u201316 October 2016, Springer International Publishing."},{"key":"ref_46","unstructured":"Leal-Taix\u00e9, L., and Roth, S. (2019). VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results. ECCV 2018 Workshops: Proceedings of the Computer Vision, Munich, Germany, 8\u201314 September 2018, Springer International Publishing."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, N., Shi, J., Yeung, D.Y., and Jia, J. (2015, January 7\u201313). Understanding and Diagnosing Visual Tracking Systems. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.355"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., and Felsberg, M. (2017, January 21\u201326). ECO: Efficient Convolution Operators for Tracking. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.733"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H.S. (2016, January 27\u201330). Staple: Complementary Learners for Real-Time Tracking. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.156"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dai, K., Wang, D., Lu, H., Sun, C., and Li, J. (2019, January 15\u201320). Visual Tracking via Adaptive Spatially-Regularized Correlation Filters. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00480"},{"key":"ref_51","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Unveiling the Power of Deep Tracking. ECCV 2018: Proceedings of the Computer Vision, Munich, Germany, 8\u201314 September 2018, Springer International Publishing."},{"key":"ref_52","doi-asserted-by":"crossref","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 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00804"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J. (2011). Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Now Foundations and Trends.","DOI":"10.1561\/9781601984616"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/LSP.2017.2681687","article-title":"Color Feature Reinforcement for Cosaliency Detection Without Single Saliency Residuals","volume":"24","author":"Huang","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1109\/TCSVT.2018.2859773","article-title":"Weakly Supervised Salient Object Detection With Spatiotemporal Cascade Neural Networks","volume":"29","author":"Tang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wei, Y., Feng, J., Liang, X., Cheng, M.M., Zhao, Y., and Yan, S. (2017, January 21\u201326). Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.687"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, X., You, S., Li, X., and Ma, H. (2018, January 18\u201323). Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00147"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Hou, X., and Zhang, L. (2007, January 17\u201322). Saliency Detection: A Spectral Residual Approach. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383267"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Y., Zhou, W., Shi, L., and Li, D. (2018). Motion-Aware Correlation Filters for Online Visual Tracking. Sensors, 18.","DOI":"10.3390\/s18113937"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2017, January 21\u201326). Context-Aware Correlation Filter Tracking. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.152"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, F., Tian, C., Zuo, W., Zhang, L., and Yang, M.H. (2018, January 18\u201323). Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00515"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3546","DOI":"10.1109\/TIP.2019.2962694","article-title":"SITUP: Scale Invariant Tracking Using Average Peak-to-Correlation Energy","volume":"29","author":"Ma","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","article-title":"Object Tracking Benchmark","volume":"37","author":"Wu","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. 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