{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:29:00Z","timestamp":1774448940839,"version":"3.50.1"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031189159","type":"print"},{"value":"9783031189166","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18916-6_56","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"707-720","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["WTB-LLL: A Watercraft Tracking Benchmark Derived by\u00a0Low-Light-Level Camera"],"prefix":"10.1007","author":[{"given":"Chongyi","family":"Ye","sequence":"first","affiliation":[]},{"given":"Yuzhang","family":"Gu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"56_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1007\/978-3-319-48881-3_56","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"L Bertinetto","year":"2016","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850\u2013865. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_56"},{"key":"56_CR2","doi-asserted-by":"crossref","unstructured":"Bhat, G., Danelljan, M., Gool, L.V., Timofte, R.: Learning discriminative model prediction for tracking. In: Proceedings of the IEEE\/CVF International Conference on Computer vision, pp. 6182\u20136191 (2019)","DOI":"10.1109\/ICCV.2019.00628"},{"key":"56_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-3-030-58592-1_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Bhat","year":"2020","unstructured":"Bhat, G., Danelljan, M., Van Gool, L., Timofte, R.: Know your surroundings: exploiting scene information for object tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 205\u2013221. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_13"},{"key":"56_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., Lu, H.: Transformer tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8126\u20138135 (2021)","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"56_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhong, B., Li, G., Zhang, S., Ji, R.: Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 6668\u20136677 (2020)","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"56_CR6","doi-asserted-by":"crossref","unstructured":"Dai, K., Wang, D., Lu, H., Sun, C., Li, J.: Visual tracking via adaptive spatially-regularized correlation filters. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4670\u20134679 (2019)","DOI":"10.1109\/CVPR.2019.00480"},{"key":"56_CR7","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Atom: Accurate tracking by overlap maximization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4660\u20134669 (2019)","DOI":"10.1109\/CVPR.2019.00479"},{"key":"56_CR8","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Gool, L.V., Timofte, R.: Probabilistic regression for visual tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 7183\u20137192 (2020)","DOI":"10.1109\/CVPR42600.2020.00721"},{"key":"56_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-319-46454-1_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Danelljan","year":"2016","unstructured":"Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 472\u2013488. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_29"},{"key":"56_CR10","doi-asserted-by":"crossref","unstructured":"Fan, H., et al.: LaSOT: A high-quality benchmark for large-scale single object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5374\u20135383 (2019)","DOI":"10.1109\/CVPR.2019.00552"},{"key":"56_CR11","doi-asserted-by":"crossref","unstructured":"Guo, D., Shao, Y., Cui, Y., Wang, Z., Zhang, L., Shen, C.: Graph attention tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9543\u20139552 (2021)","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"56_CR12","doi-asserted-by":"crossref","unstructured":"Guo, D., Wang, J., Cui, Y., Wang, Z., Chen, S.: SiamCar: Siamese fully convolutional classification and regression for visual tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6269\u20136277 (2020)","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"56_CR13","unstructured":"Hadfield, S., Lebeda, K., Bowden, R.: The visual object tracking vot2014 challenge results. In: European Conference on Computer Vision (ECCV) Visual Object Tracking Challenge Workshop. University of Surrey (2014)"},{"key":"56_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"56_CR15","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TPAMI.2019.2957464","volume":"43","author":"L Huang","year":"2019","unstructured":"Huang, L., Zhao, X., Huang, K.: Got-10k: a large high-diversity benchmark for generic object tracking in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1562\u20131577 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"56_CR16","doi-asserted-by":"crossref","unstructured":"Kiani Galoogahi, H., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: Need for speed: A benchmark for higher frame rate object tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1125\u20131134 (2017)","DOI":"10.1109\/ICCV.2017.128"},{"key":"56_CR17","unstructured":"Lebeda, K., Hadfield, S., Bowden, R., et al.: The thermal infrared visual object tracking VOT-TIR2016 challenge result. In: Proceedings, European Conference on Computer Vision (ECCV) workshops. University of Surrey (2016)"},{"key":"56_CR18","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: SiamRPN++: Evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4282\u20134291 (2019)","DOI":"10.1109\/CVPR.2019.00441"},{"key":"56_CR19","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8971\u20138980 (2018)","DOI":"10.1109\/CVPR.2018.00935"},{"key":"56_CR20","doi-asserted-by":"crossref","unstructured":"Li, B., Fu, C., Ding, F., Ye, J., Lin, F.: Adtrack: Target-aware dual filter learning for real-time anti-dark UAV tracking. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 496\u2013502. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561564"},{"key":"56_CR21","doi-asserted-by":"crossref","unstructured":"Li, B., Fu, C., Ding, F., Ye, J., Lin, F.: All-day object tracking for unmanned aerial vehicle. IEEE Transactions on Mobile Computing (2022)","DOI":"10.1109\/TMC.2022.3162892"},{"issue":"12","key":"56_CR22","first-page":"2057","volume":"24","author":"Q Li","year":"2019","unstructured":"Li, Q., et al.: Survey of visual object tracking algorithms based on deep learning. J. Image. Graph. 24(12), 2057\u20132080 (2019)","journal-title":"J. Image. Graph."},{"key":"56_CR23","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.knosys.2018.12.011","volume":"166","author":"X Li","year":"2019","unstructured":"Li, X., Liu, Q., Fan, N., He, Z., Wang, H.: Hierarchical spatial-aware siamese network for thermal infrared object tracking. Knowledge-Based Syst. 166, 71\u201381 (2019)","journal-title":"Knowledge-Based Syst."},{"issue":"3","key":"56_CR24","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/TMM.2019.2932615","volume":"22","author":"Q Liu","year":"2019","unstructured":"Liu, Q., He, Z., Li, X., Zheng, Y.: PTB-TIR: a thermal infrared pedestrian tracking benchmark. IEEE Trans. Multimedia 22(3), 666\u2013675 (2019)","journal-title":"IEEE Trans. Multimedia"},{"key":"56_CR25","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1109\/TMM.2020.3008028","volume":"23","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Li, X., He, Z., Fan, N., Yuan, D., Wang, H.: Learning deep multi-level similarity for thermal infrared object tracking. IEEE Trans. Multimedia 23, 2114\u20132126 (2020)","journal-title":"IEEE Trans. Multimedia"},{"key":"56_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Q., et al.: LSOTB-TIR: A large-scale high-diversity thermal infrared object tracking benchmark. In: Proceedings of the 28th ACM International Conference on Multimedia. pp. 3847\u20133856 (2020)","DOI":"10.1145\/3394171.3413922"},{"key":"56_CR27","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.knosys.2017.07.032","volume":"134","author":"Q Liu","year":"2017","unstructured":"Liu, Q., Lu, X., He, Z., Zhang, C., Chen, W.S.: Deep convolutional neural networks for thermal infrared object tracking. Knowledge-Based Syst. 134, 189\u2013198 (2017)","journal-title":"Knowledge-Based Syst."},{"key":"56_CR28","doi-asserted-by":"crossref","unstructured":"Mayer, C., Danelljan, M., Paudel, D.P., Van Gool, L.: Learning target candidate association to keep track of what not to track. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13444\u201313454 (2021)","DOI":"10.1109\/ICCV48922.2021.01319"},{"key":"56_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/978-3-319-46448-0_27","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Mueller","year":"2016","unstructured":"Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445\u2013461. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_27"},{"key":"56_CR30","doi-asserted-by":"crossref","unstructured":"Muller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: TrackingNet: A large-scale dataset and benchmark for object tracking in the wild. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 300\u2013317 (2018)","DOI":"10.1007\/978-3-030-01246-5_19"},{"key":"56_CR31","unstructured":"Shiming, X., Xuewu, F., Na, H., Zhe, B., et al.: Review on low light level remote sensing imaging technology (2018)"},{"key":"56_CR32","doi-asserted-by":"crossref","unstructured":"Valmadre, J., et al.: Long-term tracking in the wild: A benchmark. In: Proceedings of the European conference on computer vision (ECCV), pp. 670\u2013685 (2018)","DOI":"10.1007\/978-3-030-01219-9_41"},{"issue":"1","key":"56_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-015-9454-6","volume":"46","author":"GS Walia","year":"2016","unstructured":"Walia, G.S., Kapoor, R.: Recent advances on multicue object tracking: a survey. Artif. Intell. Rev. 46(1), 1\u201339 (2016). https:\/\/doi.org\/10.1007\/s10462-015-9454-6","journal-title":"Artif. Intell. Rev."},{"key":"56_CR34","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhou, W., Wang, J., Li, H.: Transformer meets tracker: Exploiting temporal context for robust visual tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1571\u20131580 (2021)","DOI":"10.1109\/CVPR46437.2021.00162"},{"key":"56_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: A unifying approach. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition, pp. 1328\u20131338 (2019)","DOI":"10.1109\/CVPR.2019.00142"},{"issue":"9","key":"56_CR36","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu, Y., Lim, J., Yang, M.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834\u20131848 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"56_CR37","doi-asserted-by":"crossref","unstructured":"Yan, B., Peng, H., Fu, J., Wang, D., Lu, H.: Learning spatio-temporal transformer for visual tracking. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10448\u201310457 (2021)","DOI":"10.1109\/ICCV48922.2021.01028"},{"issue":"2","key":"56_CR38","doi-asserted-by":"publisher","first-page":"3866","DOI":"10.1109\/LRA.2022.3146911","volume":"7","author":"J Ye","year":"2022","unstructured":"Ye, J., Fu, C., Cao, Z., An, S., Zheng, G., Li, B.: Tracker meets night: a transformer enhancer for UAV tracking. IEEE Robot. Autom. Lett. 7(2), 3866\u20133873 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"56_CR39","doi-asserted-by":"crossref","unstructured":"Ye, J., Fu, C., Zheng, G., Cao, Z., Li, B.: Darklighter: Light up the darkness for uav tracking. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3079\u20133085. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9636680"},{"key":"56_CR40","doi-asserted-by":"crossref","unstructured":"Yu, Y., Xiong, Y., Huang, W., Scott, M.R.: Deformable siamese attention networks for visual object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6728\u20136737 (2020)","DOI":"10.1109\/CVPR42600.2020.00676"},{"key":"56_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Peng, H.: Deeper and wider siamese networks for real-time visual tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4591\u20134600 (2019)","DOI":"10.1109\/CVPR.2019.00472"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18916-6_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T16:36:51Z","timestamp":1728232611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18916-6_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189159","9783031189166"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18916-6_56","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"233","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.03","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.35","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}