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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.<\/jats:p>","DOI":"10.1145\/3651308","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:50:56Z","timestamp":1709812256000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Review and Analysis of RGBT Single Object Tracking Methods: A Fusion Perspective"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8101-2489","authenticated-orcid":false,"given":"Zhihao","family":"Zhang","sequence":"first","affiliation":[{"name":"National Innovation Institute of Defense Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7123-9461","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"National Innovation Institute of Defense Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7250-9840","authenticated-orcid":false,"given":"Shengjie","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4855-2464","authenticated-orcid":false,"given":"Lei","family":"Jin","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5309-6101","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Normal University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3508-756X","authenticated-orcid":false,"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, China &amp; China Telecom AI Institute, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5183-9867","authenticated-orcid":false,"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Innovation Institute of Defense Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1401","volume-title":"CVPR","author":"Bertinetto Luca","year":"2016","unstructured":"Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, and Philip H. 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In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2544\u20132550."},{"key":"e_1_3_1_6_2","first-page":"35","volume-title":"WACV","author":"Bunyak Filiz","year":"2007","unstructured":"Filiz Bunyak, Kannappan Palaniappan, Sumit Kumar Nath, and Guna Seetharaman. 2007. Geodesic active contour based fusion of visible and infrared video for persistent object tracking. In WACV. 35\u201335."},{"key":"e_1_3_1_7_2","first-page":"8126","volume-title":"CVPR","author":"Chen Xin","year":"2021","unstructured":"Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang, and Huchuan Lu. 2021. Transformer tracking. In CVPR. 8126\u20138135."},{"key":"e_1_3_1_8_2","article-title":"Visual tracking by reinforced decision making","volume":"2","author":"Choi Janghoon","year":"2017","unstructured":"Janghoon Choi, Junseok Kwon, and Kyoung Mu Lee. 2017. Visual tracking by reinforced decision making. arXiv preprint arXiv:1702.06291 2 (2017).","journal-title":"arXiv preprint arXiv:1702.06291"},{"key":"e_1_3_1_9_2","first-page":"911","volume-title":"ICCV","author":"Choi Janghoon","year":"2019","unstructured":"Janghoon Choi, Junseok Kwon, and Kyoung Mu Lee. 2019. Deep meta learning for real-time target-aware visual tracking. In ICCV. 911\u2013920."},{"key":"e_1_3_1_10_2","first-page":"1","volume-title":"ICIF","author":"Conaire Ciar\u00e1n O.","year":"2006","unstructured":"Ciar\u00e1n O. Conaire, Noel E. O\u2019Connor, Eddie Cooke, and Alan F. Smeaton. 2006. Comparison of fusion methods for thermo-visual surveillance tracking. In ICIF. 1\u20137."},{"key":"e_1_3_1_11_2","first-page":"483","article-title":"Thermo-visual feature fusion for object tracking using multiple spatiogram trackers","author":"Conaire Ciar\u00e1n \u00d3","year":"2008","unstructured":"Ciar\u00e1n \u00d3 Conaire, Noel E. O\u2019Connor, and Alan Smeaton. 2008. 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In CVPR, Vol. 1. 886\u2013893."},{"key":"e_1_3_1_15_2","first-page":"4660","volume-title":"CVPR","author":"Danelljan Martin","year":"2019","unstructured":"Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, and Michael Felsberg. 2019. ATOM: Accurate tracking by overlap maximization. In CVPR. 4660\u20134669."},{"key":"e_1_3_1_16_2","first-page":"6638","volume-title":"CVPR","author":"Danelljan Martin","year":"2017","unstructured":"Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, and Michael Felsberg. 2017. ECO: Efficient convolution operators for tracking. In CVPR. 6638\u20136646."},{"key":"e_1_3_1_17_2","volume-title":"BMVC","author":"Danelljan Martin","year":"2014","unstructured":"Martin Danelljan, Gustav H\u00e4ger, Fahad Khan, and Michael Felsberg. 2014. Accurate scale estimation for robust visual tracking. In BMVC."},{"issue":"8","key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","article-title":"Discriminative scale space tracking","volume":"39","author":"Danelljan Martin","year":"2016","unstructured":"Martin Danelljan, Gustav H\u00e4ger, Fahad Shahbaz Khan, and Michael Felsberg. 2016. Discriminative scale space tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 8 (2016), 1561\u20131575.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_19_2","first-page":"58","volume-title":"ICCVW","author":"Danelljan Martin","year":"2015","unstructured":"Martin Danelljan, Gustav Hager, Fahad Shahbaz Khan, and Michael Felsberg. 2015. Convolutional features for correlation filter based visual tracking. In ICCVW. 58\u201366."},{"key":"e_1_3_1_20_2","first-page":"4310","volume-title":"ICCV","author":"Danelljan Martin","year":"2015","unstructured":"Martin Danelljan, Gustav Hager, Fahad Shahbaz Khan, and Michael Felsberg. 2015. Learning spatially regularized correlation filters for visual tracking. In ICCV. 4310\u20134318."},{"key":"e_1_3_1_21_2","first-page":"1430","volume-title":"CVPR","author":"Danelljan Martin","year":"2016","unstructured":"Martin Danelljan, Gustav Hager, Fahad Shahbaz Khan, and Michael Felsberg. 2016. Adaptive decontamination of the training set: A unified formulation for discriminative visual tracking. In CVPR. 1430\u20131438."},{"key":"e_1_3_1_22_2","first-page":"472","volume-title":"ECCV","author":"Danelljan Martin","year":"2016","unstructured":"Martin Danelljan, Andreas Robinson, Fahad Shahbaz Khan, and Michael Felsberg. 2016. Beyond correlation filters: Learning continuous convolution operators for visual tracking. 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In ICCVW. 91\u201399."},{"issue":"7","key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.1007\/s00371-021-02131-4","article-title":"Dual Siamese network for RGBT tracking via fusing predicted position maps","volume":"38","author":"Guo Chang","year":"2022","unstructured":"Chang Guo, Dedong Yang, Chang Li, and Peng Song. 2022. Dual Siamese network for RGBT tracking via fusing predicted position maps. TVC 38, 7 (2022), 2555\u20132567.","journal-title":"TVC"},{"key":"e_1_3_1_29_2","first-page":"9543","volume-title":"CVPR","author":"Guo Dongyan","year":"2021","unstructured":"Dongyan Guo, Yanyan Shao, Ying Cui, Zhenhua Wang, Liyan Zhang, and Chunhua Shen. 2021. Graph attention tracking. In CVPR. 9543\u20139552."},{"key":"e_1_3_1_30_2","first-page":"6269","volume-title":"CVPR","year":"2020","unstructured":"Ying Cui, Zhenhua Wang, Shengyong Chen, Jun Wang, and Dongyan Guo. 2020. SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In CVPR. 6269\u20136277."},{"key":"e_1_3_1_31_2","first-page":"1763","volume-title":"ICCV","author":"Guo Qing","year":"2017","unstructured":"Qing Guo, Wei Feng, Ce Zhou, Rui Huang, Liang Wan, and Song Wang. 2017. Learning dynamic siamese network for visual object tracking. In ICCV. 1763\u20131771."},{"key":"e_1_3_1_32_2","first-page":"702","volume-title":"ECCV","author":"Henriques Joao F.","year":"2012","unstructured":"Joao F. Henriques, Rui Caseiro, Pedro Martins, and Jorge Batista. 2012. Exploiting the circulant structure of tracking-by-detection with kernels. In ECCV. Springer, 702\u2013715."},{"key":"e_1_3_1_33_2","first-page":"1163","volume-title":"ICME","author":"Hou Ruichao","year":"2023","unstructured":"Ruichao Hou, Boyue Xu, Tongwei Ren, and Gangshan Wu. 2023. MTNet: Learning modality-aware representation with transformer for RGBT tracking. In ICME. 1163\u20131168."},{"key":"e_1_3_1_34_2","first-page":"11037","volume-title":"AAAI","author":"Huang Lianghua","year":"2020","unstructured":"Lianghua Huang, Xin Zhao, and Kaiqi Huang. 2020. Globaltrack: A simple and strong baseline for long-term tracking. In AAAI, Vol. 34. 11037\u201311044."},{"issue":"1","key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1008078328650","article-title":"CONDENSATION\u2013conditional density propagation for visual tracking","volume":"29","author":"Isard Michael","year":"1998","unstructured":"Michael Isard and Andrew Blake. 1998. CONDENSATION\u2013conditional density propagation for visual tracking. IJCV 29, 1 (1998), 5.","journal-title":"IJCV"},{"key":"e_1_3_1_36_2","first-page":"6668","volume-title":"CVPR","author":"Ji Zedu Chen, Bineng Zhong,Guorong Li, Shengping Zhang, and Rongrong","year":"2020","unstructured":"Zedu Chen, Bineng Zhong,Guorong Li, Shengping Zhang, and Rongrong Ji. 2020. Siamese box adaptive network for visual tracking. In CVPR. 6668\u20136677."},{"key":"e_1_3_1_37_2","article-title":"Siamese infrared and visible light fusion network for RGB-T tracking","author":"Jingchao Peng","year":"2021","unstructured":"Peng Jingchao, Zhao Haitao, Hu Zhengwei, Zhuang Yi, and Wang Bofan. 2021. Siamese infrared and visible light fusion network for RGB-T tracking. arXiv preprint arXiv:2103.07302 (2021).","journal-title":"arXiv preprint arXiv:2103.07302"},{"key":"e_1_3_1_38_2","first-page":"2262","volume-title":"ICCVW","author":"Kristan Matej","year":"2019","unstructured":"Matej Kristan, Jiri Matas, Ales Leonardis, Michael Felsberg, Roman Pflugfelder, Joni-Kristian Kamarainen, Luka \u010cehovin Zajc, Ondrej Drbohlav, Alan Lukezic, Amanda Berg, et\u00a0al. 2019. The seventh visual object tracking vot2019 challenge results. In ICCVW. 2262\u20132270."},{"key":"e_1_3_1_39_2","first-page":"12","article-title":"Modality-correlation-aware sparse representation for RGB-infrared object tracking","volume":"130","author":"Lan Xiangyuan","year":"2020","unstructured":"Xiangyuan Lan, Mang Ye, Shengping Zhang, Huiyu Zhou, and Pong C. Yuen. 2020. Modality-correlation-aware sparse representation for RGB-infrared object tracking. PRL 130 (2020), 12\u201320.","journal-title":"PRL"},{"key":"e_1_3_1_40_2","first-page":"4282","volume-title":"CVPR","author":"Li Bo","year":"2019","unstructured":"Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, and Junjie Yan. 2019. SiamRPN++: Evolution of Siamese visual tracking with very deep networks. In CVPR. 4282\u20134291."},{"issue":"7","key":"e_1_3_1_41_2","first-page":"3624","article-title":"Learning to update for object tracking with recurrent meta-learner","volume":"28","author":"Li Bi","year":"2019","unstructured":"Bi Li, Wenxuan Xie, Wenjun Zeng, and Wenyu Liu. 2019. Learning to update for object tracking with recurrent meta-learner. TIP 28, 7 (2019), 3624\u20133635.","journal-title":"TIP"},{"key":"e_1_3_1_42_2","first-page":"8971","volume-title":"CVPR","author":"Li Bo","year":"2018","unstructured":"Bo Li, Junjie Yan, Wei Wu, Zheng Zhu, and Xiaolin Hu. 2018. High performance visual tracking with Siamese region proposal network. In CVPR. 8971\u20138980."},{"issue":"12","key":"e_1_3_1_43_2","first-page":"5743","article-title":"Learning collaborative sparse representation for grayscale-thermal tracking","volume":"25","author":"Li Chenglong","year":"2016","unstructured":"Chenglong Li, Hui Cheng, Shiyi Hu, Xiaobai Liu, Jin Tang, and Liang Lin. 2016. Learning collaborative sparse representation for grayscale-thermal tracking. TIP 25, 12 (2016), 5743\u20135756.","journal-title":"TIP"},{"key":"e_1_3_1_44_2","first-page":"54","volume-title":"MMM","author":"Li Chenglong","year":"2016","unstructured":"Chenglong Li, Shiyi Hu, Sihan Gao, and Jin Tang. 2016. Real-time grayscale-thermal tracking via Laplacian sparse representation. In MMM. Springer, 54\u201365."},{"key":"e_1_3_1_45_2","article-title":"RGB-T object tracking: Benchmark and baseline","volume":"1805","author":"Li Chenglong","year":"2018","unstructured":"Chenglong Li, Xinyan Liang, Yijuan Lu, Nan Zhao, and Jin Tang. 2018. RGB-T object tracking: Benchmark and baseline. CoRR abs\/1805.08982 (2018).","journal-title":"CoRR"},{"key":"e_1_3_1_46_2","first-page":"106977","article-title":"RGB-T object tracking: Benchmark and baseline","volume":"96","author":"Li Chenglong","year":"2019","unstructured":"Chenglong Li, Xinyan Liang, Yijuan Lu, Nan Zhao, and Jin Tang. 2019. RGB-T object tracking: Benchmark and baseline. PR 96 (2019), 106977.","journal-title":"PR"},{"key":"e_1_3_1_47_2","volume-title":"AAAI","author":"Li Chenglong","year":"2017","unstructured":"Chenglong Li, Liang Lin, Wangmeng Zuo, and Jin Tang. 2017. Learning patch-based dynamic graph for visual tracking. In AAAI, Vol. 31."},{"issue":"11","key":"e_1_3_1_48_2","first-page":"2770","article-title":"Visual tracking via dynamic graph learning","volume":"41","author":"Li Chenglong","year":"2018","unstructured":"Chenglong Li, Liang Lin, Wangmeng Zuo, Jin Tang, and Ming-Hsuan Yang. 2018. Visual tracking via dynamic graph learning. TPAMI 41, 11 (2018), 2770\u20132782.","journal-title":"TPAMI"},{"key":"e_1_3_1_49_2","first-page":"222","volume-title":"ECCV","author":"Li Chenglong","year":"2020","unstructured":"Chenglong Li, Lei Liu, Andong Lu, Qing Ji, and Jin Tang. 2020. Challenge-aware RGBT tracking. In ECCV. Springer, 222\u2013237."},{"issue":"4","key":"e_1_3_1_50_2","first-page":"673","article-title":"Grayscale-thermal object tracking via multitask Laplacian sparse representation","volume":"47","author":"Li Chenglong","year":"2017","unstructured":"Chenglong Li, Xiang Sun, Xiao Wang, Lei Zhang, and Jin Tang. 2017. Grayscale-thermal object tracking via multitask Laplacian sparse representation. 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Cross-modal ranking with soft consistency and noisy labels for robust RGB-T tracking. In ECCV. 808\u2013823."},{"issue":"10","key":"e_1_3_1_56_2","first-page":"2913","article-title":"Learning local-global multi-graph descriptors for RGB-T object tracking","volume":"29","author":"Li Chenglong","year":"2018","unstructured":"Chenglong Li, Chengli Zhu, Jian Zhang, Bin Luo, Xiaohao Wu, and Jin Tang. 2018. Learning local-global multi-graph descriptors for RGB-T object tracking. TCSVT 29, 10 (2018), 2913\u20132926.","journal-title":"TCSVT"},{"key":"e_1_3_1_57_2","first-page":"207","article-title":"Two-stage modality-graphs regularized manifold ranking for RGB-T tracking","volume":"68","author":"Li Chenglong","year":"2018","unstructured":"Chenglong Li, Chengli Zhu, Shaofei Zheng, Bin Luo, and Jin Tang. 2018. Two-stage modality-graphs regularized manifold ranking for RGB-T tracking. 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Unsupervised RGB-T object tracking with attentional multi-modal feature fusion. Multimedia Tools and Applications (2023), 1\u201319.","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_3_1_61_2","first-page":"510","volume-title":"CVPR","author":"Li Xiang","year":"2019","unstructured":"Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang. 2019. Selective kernel networks. In CVPR. 510\u2013519."},{"key":"e_1_3_1_62_2","doi-asserted-by":"crossref","unstructured":"Yang Li and Jianke Zhu. 2014. A scale adaptive kernel correlation filter tracker with feature integration. In ECCVW Vol. 8926. Citeseer 254\u2013265.","DOI":"10.1007\/978-3-319-16181-5_18"},{"issue":"5","key":"e_1_3_1_63_2","doi-asserted-by":"crossref","first-page":"10491","DOI":"10.4249\/scholarpedia.10491","article-title":"Scale Invariant Feature Transform","volume":"7","author":"Lindeberg T.","year":"2012","unstructured":"T. Lindeberg. 2012. Scale Invariant Feature Transform. Scholarpedia 7, 5 (2012), 10491.","journal-title":"Scholarpedia"},{"key":"e_1_3_1_64_2","first-page":"3129","volume-title":"ACMM","author":"Liu Lei","year":"2023","unstructured":"Lei Liu, Chenglong Li, Yun Xiao, and Jin Tang. 2023. Quality-aware RGBT tracking via supervised reliability learning and weighted residual guidance. In ACMM. 3129\u20133137."},{"key":"e_1_3_1_65_2","first-page":"2262","volume-title":"ICCVW","author":"Li Cheng Long","year":"2019","unstructured":"Cheng Long Li, Andong Lu, Ai Hua Zheng, Zhengzheng Tu, and Jin Tang. 2019. Multi-adapter RGBT tracking. In ICCVW. 2262\u20132270."},{"key":"e_1_3_1_66_2","first-page":"5613","article-title":"RGBT tracking via multi-adapter network with hierarchical divergence loss","volume":"30","author":"Lu Andong","year":"2021","unstructured":"Andong Lu, Chenglong Li, Yuqing Yan, Jin Tang, and Bin Luo. 2021. RGBT tracking via multi-adapter network with hierarchical divergence loss. TIP 30 (2021), 5613\u20135625.","journal-title":"TIP"},{"key":"e_1_3_1_67_2","article-title":"Duality-gated mutual condition network for RGBT tracking","author":"Lu Andong","year":"2022","unstructured":"Andong Lu, Cun Qian, Chenglong Li, Jin Tang, and Liang Wang. 2022. Duality-gated mutual condition network for RGBT tracking. TNNLS (2022).","journal-title":"TNNLS"},{"key":"e_1_3_1_68_2","first-page":"10448","volume-title":"ICCV","author":"Lu Bin Yan, Houwen Peng, Jianlong Fu, Dong Wang, and Huchuan","year":"2021","unstructured":"Bin Yan, Houwen Peng, Jianlong Fu, Dong Wang, and Huchuan Lu. 2021. Learning spatio-temporal transformer for visual tracking. In ICCV. 10448\u201310457."},{"key":"e_1_3_1_69_2","first-page":"1436","volume-title":"ICCV","author":"Mei Xue","year":"2009","unstructured":"Xue Mei and Haibin Ling. 2009. Robust visual tracking using l1 minimization. In ICCV. 1436\u20131443."},{"key":"e_1_3_1_70_2","first-page":"4293","volume-title":"CVPR","author":"Nam Hyeonseob","year":"2016","unstructured":"Hyeonseob Nam and Bohyung Han. 2016. Learning multi-domain convolutional neural networks for visual tracking. In CVPR. 4293\u20134302."},{"key":"e_1_3_1_71_2","unstructured":"Renaud P\u00e9teri and Ond\u0159ej \u0160iler. 2009. Object tracking using joint visible and thermal infrared video sequences. 2009. hal-00398488 https:\/\/hal.science\/hal-00398488\/document"},{"key":"e_1_3_1_72_2","first-page":"8835","volume-title":"AAAI","author":"Qi Yuankai","year":"2019","unstructured":"Yuankai Qi, Shengping Zhang, Weigang Zhang, Li Su, Qingming Huang, and Ming-Hsuan Yang. 2019. Learning attribute-specific representations for visual tracking. In AAAI, Vol. 33. 8835\u20138842."},{"key":"e_1_3_1_73_2","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.neucom.2022.04.032","article-title":"RGBT tracking based on cooperative low-rank graph model","volume":"492","author":"Shen Longfeng","year":"2022","unstructured":"Longfeng Shen, Xiaoxiao Wang, Lei Liu, Bin Hou, Yulei Jian, Jin Tang, and Bin Luo. 2022. RGBT tracking based on cooperative low-rank graph model. Neurocomputing 492 (2022), 370\u2013381.","journal-title":"Neurocomputing"},{"issue":"7","key":"e_1_3_1_74_2","first-page":"1442","article-title":"Visual tracking: An experimental survey","volume":"36","author":"Smeulders Arnold W. M.","year":"2013","unstructured":"Arnold W. M. Smeulders, Dung M. Chu, Rita Cucchiara, Simone Calderara, Afshin Dehghan, and Mubarak Shah. 2013. Visual tracking: An experimental survey. TPAMI 36, 7 (2013), 1442\u20131468.","journal-title":"TPAMI"},{"key":"e_1_3_1_75_2","first-page":"2555","volume-title":"ICCV","author":"Song Yibing","year":"2017","unstructured":"Yibing Song, Chao Ma, Lijun Gong, Jiawei Zhang, Rynson W. H. Lau, and Ming-Hsuan Yang. 2017. Crest: Convolutional residual learning for visual tracking. In ICCV. 2555\u20132564."},{"key":"e_1_3_1_76_2","article-title":"A survey for deep RGBT tracking","author":"Tang Zhangyong","year":"2022","unstructured":"Zhangyong Tang, Tianyang Xu, and Xiao-Jun Wu. 2022. A survey for deep RGBT tracking. arXiv preprint arXiv:2201.09296 (2022).","journal-title":"arXiv preprint arXiv:2201.09296"},{"key":"e_1_3_1_77_2","first-page":"85","article-title":"M 5 l: Multi-modal multi-margin metric learning for RGBT tracking","volume":"31","author":"Tu Zhengzheng","year":"2021","unstructured":"Zhengzheng Tu, Chun Lin, Wei Zhao, Chenglong Li, and Jin Tang. 2021. M 5 l: Multi-modal multi-margin metric learning for RGBT tracking. TIP 31 (2021), 85\u201398.","journal-title":"TIP"},{"key":"e_1_3_1_78_2","first-page":"6578","volume-title":"CVPR","author":"Voigtlaender Paul","year":"2020","unstructured":"Paul Voigtlaender, Jonathon Luiten, Philip H. S. Torr, and Bastian Leibe. 2020. Siam R-CNN: Visual tracking by re-detection. In CVPR. 6578\u20136588."},{"key":"e_1_3_1_79_2","first-page":"7064","volume-title":"CVPR","author":"Wang Chaoqun","year":"2020","unstructured":"Chaoqun Wang, Chunyan Xu, Zhen Cui, Ling Zhou, Tong Zhang, Xiaoya Zhang, and Jian Yang. 2020. Cross-modal pattern-propagation for RGB-T tracking. In CVPR. 7064\u20137073."},{"key":"e_1_3_1_80_2","first-page":"3119","volume-title":"ICCV","author":"Wang Lijun","year":"2015","unstructured":"Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu. 2015. Visual tracking with fully convolutional networks. In ICCV. 3119\u20133127."},{"key":"e_1_3_1_81_2","first-page":"295","volume-title":"CVPR","author":"Wang Yulong","year":"2018","unstructured":"Yulong Wang, Chenglong Li, and Jin Tang. 2018. Learning soft-consistent correlation filters for RGB-T object tracking. In CVPR. 295\u2013306."},{"key":"e_1_3_1_82_2","first-page":"1","volume-title":"ICIF","author":"Wu Yi","year":"2011","unstructured":"Yi Wu, Erik Blasch, Genshe Chen, Li Bai, and Haibin Ling. 2011. Multiple source data fusion via sparse representation for robust visual tracking. In ICIF. IEEE, 1\u20138."},{"key":"e_1_3_1_83_2","first-page":"13608","volume-title":"CVPR","author":"Wu Yutao Cui, Cheng Jiang, Limin Wang, and Gangshan","year":"2022","unstructured":"Yutao Cui, Cheng Jiang, Limin Wang, and Gangshan Wu. 2022. Mixformer: End-to-end tracking with iterative mixed attention. In CVPR. 13608\u201313618."},{"key":"e_1_3_1_84_2","doi-asserted-by":"crossref","unstructured":"Gang Xiao Xiao Yun and Jianmin Wu. 2012. A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion. Sci. China Inform. Sci. 55 (2012) 577\u2013589.","DOI":"10.1007\/s11432-012-4553-3"},{"key":"e_1_3_1_85_2","first-page":"40","article-title":"A new tracking approach for visible and infrared sequences based on tracking-before-fusion","volume":"4","author":"Xiao Gang","year":"2016","unstructured":"Gang Xiao, Xiao Yun, and Jianmin Wu. 2016. A new tracking approach for visible and infrared sequences based on tracking-before-fusion. IJDC 4 (2016), 40\u201351.","journal-title":"IJDC"},{"issue":"7","key":"e_1_3_1_86_2","doi-asserted-by":"crossref","first-page":"3410","DOI":"10.3390\/s23073410","article-title":"Multi-scale feature interactive fusion network for RGBT tracking","volume":"23","author":"Xiao Xianbing","year":"2023","unstructured":"Xianbing Xiao, Xingzhong Xiong, Fanqin Meng, and Zhen Chen. 2023. Multi-scale feature interactive fusion network for RGBT tracking. Sensors 23, 7 (2023), 3410.","journal-title":"Sensors"},{"issue":"012106","key":"e_1_3_1_87_2","first-page":"1","article-title":"A compressive tracking based on time-space Kalman fusion model","volume":"59","author":"Xiao Yun","year":"2016","unstructured":"Yun Xiao, Zhongliang Jing, Gang Xiao, Jin Bo, and Canlong Zhang. 2016. A compressive tracking based on time-space Kalman fusion model. Information Sciences 59, 012106 (2016), 1\u2013012106.","journal-title":"Information Sciences"},{"key":"e_1_3_1_88_2","first-page":"2831","volume-title":"AAAI","author":"Xiao Yun","year":"2022","unstructured":"Yun Xiao, Mengmeng Yang, Chenglong Li, Lei Liu, and Jin Tang. 2022. Attribute-based progressive fusion network for RGBT tracking. In AAAI, Vol. 36. 2831\u20132838."},{"key":"e_1_3_1_89_2","first-page":"567","article-title":"Multimodal cross-layer bilinear pooling for RGBT tracking","volume":"24","author":"Xu Qin","year":"2021","unstructured":"Qin Xu, Yiming Mei, Jinpei Liu, and Chenglong Li. 2021. Multimodal cross-layer bilinear pooling for RGBT tracking. TMM 24 (2021), 567\u2013580.","journal-title":"TMM"},{"issue":"13","key":"e_1_3_1_90_2","doi-asserted-by":"crossref","first-page":"3252","DOI":"10.3390\/rs15133252","article-title":"SiamCAF: Complementary attention fusion-based siamese network for RGBT tracking","volume":"15","author":"Xue Yingjian","year":"2023","unstructured":"Yingjian Xue, Jianwei Zhang, Zhoujin Lin, Chenglong Li, Bihan Huo, and Yan Zhang. 2023. SiamCAF: Complementary attention fusion-based siamese network for RGBT tracking. Remote Sensing 15, 13 (2023), 3252.","journal-title":"Remote Sensing"},{"key":"e_1_3_1_91_2","first-page":"152","volume-title":"ECCV","author":"Yang Tianyu","year":"2018","unstructured":"Tianyu Yang and Antoni B. Chan. 2018. Learning dynamic memory networks for object tracking. In ECCV. 152\u2013167."},{"key":"e_1_3_1_92_2","first-page":"8791","volume-title":"CVPR","author":"Yang Zikai Song, Junqing Yu, Yi-Ping (Phoebe) Chen, and Wei","year":"2022","unstructured":"Zikai Song, Junqing Yu, Yi-Ping (Phoebe) Chen, and Wei Yang. 2022. Transformer tracking with cyclic shifting window attention. In CVPR. 8791\u20138800."},{"issue":"4","key":"e_1_3_1_93_2","doi-asserted-by":"crossref","first-page":"13--es","DOI":"10.1145\/1177352.1177355","article-title":"Object tracking: A survey","volume":"38","author":"Yilmaz Alper","year":"2006","unstructured":"Alper Yilmaz, Omar Javed, and Mubarak Shah. 2006. Object tracking: A survey. ACM Computing Surveys 38, 4 (2006), 13--es.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_1_94_2","first-page":"6728","volume-title":"CVPR","author":"Yu Yuechen","year":"2020","unstructured":"Yuechen Yu, Yilei Xiong, Weilin Huang, and Matthew R. Scott. 2020. Deformable Siamese attention networks for visual object tracking. In CVPR. 6728\u20136737."},{"key":"e_1_3_1_95_2","doi-asserted-by":"crossref","unstructured":"Xiao Yun Yanjing Sun Xuanxuan Yang and Nannan Lu. 2019. Discriminative fusion correlation learning for visible and infrared tracking. Math. Prob. Eng. 2019 (2019).","DOI":"10.1155\/2019\/2437521"},{"key":"e_1_3_1_96_2","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.neucom.2019.01.022","article-title":"Fast RGB-T tracking via cross-modal correlation filters","volume":"334","author":"Zhai Sulan","year":"2019","unstructured":"Sulan Zhai, Pengpeng Shao, Xinyan Liang, and Xin Wang. 2019. Fast RGB-T tracking via cross-modal correlation filters. Neurocomputing 334 (2019), 172\u2013181.","journal-title":"Neurocomputing"},{"key":"e_1_3_1_97_2","article-title":"Dual-modality space-time memory network for RGBT tracking","author":"Zhang Fan","year":"2023","unstructured":"Fan Zhang, Hanwei Peng, Lingli Yu, Yuqian Zhao, and Baifan Chen. 2023. Dual-modality space-time memory network for RGBT tracking. IEEE Transactions on Instrumentation and Measurement (2023).","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"2","key":"e_1_3_1_98_2","doi-asserted-by":"crossref","first-page":"393","DOI":"10.3390\/s20020393","article-title":"Object tracking in RGB-T videos using modal-aware attention network and competitive learning","volume":"20","author":"Zhang Hui","year":"2020","unstructured":"Hui Zhang, Lei Zhang, Li Zhuo, and Jing Zhang. 2020. Object tracking in RGB-T videos using modal-aware attention network and competitive learning. Sensors 20, 2 (2020), 393.","journal-title":"Sensors"},{"issue":"10","key":"e_1_3_1_99_2","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1109\/TPAMI.2014.2315808","article-title":"Fast compressive tracking","volume":"36","author":"Zhang Kaihua","year":"2014","unstructured":"Kaihua Zhang, Lei Zhang, and Ming-Hsuan Yang. 2014. Fast compressive tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 10 (2014), 2002\u20132015.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_100_2","first-page":"2252","volume-title":"ICCVW","author":"Zhang Lichao","year":"2019","unstructured":"Lichao Zhang, Martin Danelljan, Abel Gonzalez-Garcia, Joost van de Weijer, and Fahad Shahbaz Khan. 2019. Multi-modal fusion for end-to-end RGB-T tracking. In ICCVW. 2252\u20132261."},{"key":"e_1_3_1_101_2","first-page":"4010","volume-title":"ICCV","author":"Zhang Lichao","year":"2019","unstructured":"Lichao Zhang, Abel Gonzalez-Garcia, Joost van de Weijer, Martin Danelljan, and Fahad Shahbaz Khan. 2019. Learning the model update for siamese trackers. In ICCV. 4010\u20134019."},{"key":"e_1_3_1_102_2","article-title":"Multi-modal visual tracking: Review and experimental comparison","author":"Zhang Pengyu","year":"2020","unstructured":"Pengyu Zhang, Dong Wang, and Huchuan Lu. 2020. Multi-modal visual tracking: Review and experimental comparison. arXiv preprint arXiv:2012.04176 (2020).","journal-title":"arXiv preprint arXiv:2012.04176"},{"key":"e_1_3_1_103_2","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1007\/s11263-021-01495-3","article-title":"Learning adaptive attribute-driven representation for real-time RGB-T tracking","volume":"129","author":"Zhang Pengyu","year":"2021","unstructured":"Pengyu Zhang, Dong Wang, Huchuan Lu, and Xiaoyun Yang. 2021. Learning adaptive attribute-driven representation for real-time RGB-T tracking. IJCV 129 (2021), 2714\u20132729.","journal-title":"IJCV"},{"key":"e_1_3_1_104_2","first-page":"3335","article-title":"Jointly modeling motion and appearance cues for robust RGB-T tracking","volume":"30","author":"Zhang Pengyu","year":"2021","unstructured":"Pengyu Zhang, Jie Zhao, Chunjuan Bo, Dong Wang, Huchuan Lu, and Xiaoyun Yang. 2021. Jointly modeling motion and appearance cues for robust RGB-T tracking. TIP 30 (2021), 3335\u20133347.","journal-title":"TIP"},{"key":"e_1_3_1_105_2","first-page":"8886","volume-title":"CVPR","author":"Zhang Pengyu","year":"2022","unstructured":"Pengyu Zhang, Jie Zhao, Dong Wang, Huchuan Lu, and Xiang Ruan. 2022. Visible-thermal UAV tracking: A large-scale benchmark and new baseline. In CVPR. 8886\u20138895."},{"issue":"3","key":"e_1_3_1_106_2","first-page":"1403","article-title":"SiamCDA: Complementarity- and distractor-aware RGB-T tracking based on Siamese network","volume":"32","author":"Zhang Tianlu","year":"2021","unstructured":"Tianlu Zhang, Xueru Liu, Qiang Zhang, and Jungong Han. 2021. SiamCDA: Complementarity- and distractor-aware RGB-T tracking based on Siamese network. TCSVT 32, 3 (2021), 1403\u20131417.","journal-title":"TCSVT"},{"key":"e_1_3_1_107_2","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.inffus.2020.05.002","article-title":"Object fusion tracking based on visible and infrared images: A comprehensive review","volume":"63","author":"Zhang Xingchen","year":"2020","unstructured":"Xingchen Zhang, Ping Ye, Henry Leung, Ke Gong, and Gang Xiao. 2020. Object fusion tracking based on visible and infrared images: A comprehensive review. Information Fusion 63 (2020), 166\u2013187.","journal-title":"Information Fusion"},{"key":"e_1_3_1_108_2","doi-asserted-by":"crossref","first-page":"122122","DOI":"10.1109\/ACCESS.2019.2936914","article-title":"SiamFT: An RGB-infrared fusion tracking method via fully convolutional Siamese networks","volume":"7","author":"Zhang Xingchen","year":"2019","unstructured":"Xingchen Zhang, Ping Ye, Shengyun Peng, Jun Liu, Ke Gong, and Gang Xiao. 2019. SiamFT: An RGB-infrared fusion tracking method via fully convolutional Siamese networks. IEEE Access 7 (2019), 122122\u2013122133.","journal-title":"IEEE Access"},{"key":"e_1_3_1_109_2","first-page":"115756","article-title":"DSiamMFT: An RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion","volume":"84","author":"Zhang Xingchen","year":"2020","unstructured":"Xingchen Zhang, Ping Ye, Shengyun Peng, Jun Liu, and Gang Xiao. 2020. DSiamMFT: An RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion. SPIC 84 (2020), 115756.","journal-title":"SPIC"},{"key":"e_1_3_1_110_2","article-title":"Robust online tracking with meta-updater","author":"Zhao Jie","year":"2022","unstructured":"Jie Zhao, Kenan Dai, Pengyu Zhang, Dong Wang, and Huchuan Lu. 2022. Robust online tracking with meta-updater. TPAMI (2022).","journal-title":"TPAMI"},{"key":"e_1_3_1_111_2","first-page":"9516","volume-title":"CVPR","author":"Zhu Jiawen","year":"2023","unstructured":"Jiawen Zhu, Simiao Lai, Xin Chen, Dong Wang, and Huchuan Lu. 2023. Visual prompt multi-modal tracking. In CVPR. 9516\u20139526."},{"key":"e_1_3_1_112_2","first-page":"465","volume-title":"ACMM","author":"Zhu Yabin","year":"2019","unstructured":"Yabin Zhu, Chenglong Li, Bin Luo, Jin Tang, and Xiao Wang. 2019. Dense feature aggregation and pruning for RGBT tracking. In ACMM. 465\u2013472."},{"issue":"1","key":"e_1_3_1_113_2","first-page":"121","article-title":"Quality-aware feature aggregation network for robust RGBT tracking","volume":"6","author":"Zhu Yabin","year":"2020","unstructured":"Yabin Zhu, Chenglong Li, Jin Tang, and Bin Luo. 2020. Quality-aware feature aggregation network for robust RGBT tracking. TIV 6, 1 (2020), 121\u2013130.","journal-title":"TIV"},{"issue":"2","key":"e_1_3_1_114_2","first-page":"579","article-title":"RGBT tracking by trident fusion network","volume":"32","author":"Zhu Yabin","year":"2021","unstructured":"Yabin Zhu, Chenglong Li, Jin Tang, Bin Luo, and Liang Wang. 2021. RGBT tracking by trident fusion network. TCSVT 32, 2 (2021), 579\u2013592.","journal-title":"TCSVT"},{"key":"e_1_3_1_115_2","first-page":"101","volume-title":"ECCV","author":"Zhu Zheng","year":"2018","unstructured":"Zheng Zhu, Qiang Wang, Bo Li, Wei Wu, Junjie Yan, and Weiming Hu. 2018. Distractor-aware siamese networks for visual object tracking. 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