{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:36:29Z","timestamp":1774946189996,"version":"3.50.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030208899","type":"print"},{"value":"9783030208905","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20890-5_38","type":"book-chapter","created":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T15:18:34Z","timestamp":1559402314000},"page":"595-611","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["FuCoLoT \u2013 A Fully-Correlational Long-Term Tracker"],"prefix":"10.1007","author":[{"given":"Alan","family":"Luke\u017ei\u010d","sequence":"first","affiliation":[]},{"given":"Luka \u010cehovin","family":"Zajc","sequence":"additional","affiliation":[]},{"given":"Tom\u00e1\u0161","family":"Voj\u00ed\u0159","sequence":"additional","affiliation":[]},{"given":"Ji\u0159\u00ed","family":"Matas","sequence":"additional","affiliation":[]},{"given":"Matej","family":"Kristan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,2]]},"reference":[{"key":"38_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":"38_CR2","doi-asserted-by":"crossref","unstructured":"Beyer, L., Breuers, S., Kurin, V., Leibe, B.: Towards a principled integration of multi-camera re-identification and tracking through optimal Bayes filters. CoRR abs\/1705.04608 (2017)","DOI":"10.1109\/CVPRW.2017.187"},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: Computer Vision and Pattern Recognition, pp. 2544\u20132550 (2010)","DOI":"10.1109\/CVPR.2010.5539960"},{"issue":"1","key":"38_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1\u2013122 (2011)","journal-title":"Found. Trends Mach. Learn."},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Chang, H.J., Park, M.S., Jeong, H., Choi, J.Y.: Tracking failure detection by imitating human visual perception. In: Proceedings of the International Conference on Image Processing, pp. 3293\u20133296 (2011)","DOI":"10.1109\/ICIP.2011.6116374"},{"issue":"4","key":"38_CR6","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1109\/TIP.2017.2669880","volume":"26","author":"Z Chi","year":"2017","unstructured":"Chi, Z., Li, H., Lu, H., Yang, M.H.: Dual deep network for visual tracking. IEEE Trans. Image Process. 26(4), 2005\u20132015 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"38_CR7","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886\u2013893 (2005)"},{"issue":"8","key":"38_CR8","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","volume":"39","author":"M Danelljan","year":"2017","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561\u20131575 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR9","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: Eco: efficient convolution operators for tracking. In: Computer Vision and Pattern Recognition, pp. 6638\u20136646 (2017)","DOI":"10.1109\/CVPR.2017.733"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: International Conference on Computer Vision, pp. 4310\u20134318 (2015)","DOI":"10.1109\/ICCV.2015.490"},{"key":"38_CR11","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking, pp. 1090\u20131097 (2014)","DOI":"10.1109\/CVPR.2014.143"},{"key":"38_CR12","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"},{"issue":"12","key":"38_CR13","doi-asserted-by":"publisher","first-page":"4182","DOI":"10.1109\/TCYB.2016.2626275","volume":"47","author":"D Du","year":"2017","unstructured":"Du, D., Qi, H., Wen, L., Tian, Q., Huang, Q., Lyu, S.: Geometric hypergraph learning for visual tracking. IEEE Trans. Cyber. 47(12), 4182\u20134195 (2017)","journal-title":"IEEE Trans. Cyber."},{"issue":"4","key":"38_CR14","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1109\/TIP.2017.2785626","volume":"27","author":"D Du","year":"2018","unstructured":"Du, D., Wen, L., Qi, H., Huang, Q., Tian, Q., Lyu, S.: Iterative graph seeking for object tracking. IEEE Trans. Image Process. 27(4), 1809\u20131821 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Fan, H., Ling, H.: Parallel tracking and verifying: a framework for real-time and high accuracy visual tracking. In: International Conference on Computer Vision, pp. 5486\u20135494 (2017)","DOI":"10.1109\/ICCV.2017.585"},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Sim, T., Lucey, S.: Multi-channel correlation filters. In: International Conference on Computer Vision, pp. 3072\u20133079 (2013)","DOI":"10.1109\/ICCV.2013.381"},{"key":"38_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-540-88682-2_19","volume-title":"Computer Vision \u2013 ECCV 2008","author":"H Grabner","year":"2008","unstructured":"Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 234\u2013247. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-88682-2_19"},{"issue":"3","key":"38_CR18","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2015","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583\u2013596 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., Tao, D.: Multi-store tracker (muster): a cognitive psychology inspired approach to object tracking. In: Computer Vision and Pattern Recognition, pp. 749\u2013758, June 2015","DOI":"10.1109\/CVPR.2015.7298675"},{"issue":"7","key":"38_CR20","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1109\/TPAMI.2011.239","volume":"34","author":"Z Kalal","year":"2012","unstructured":"Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409\u20131422 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Kiani Galoogahi, H., Sim, T., Lucey, S.: Correlation filters with limited boundaries. In: Computer Vision and Pattern Recognition, pp. 4630\u20134638 (2015)","DOI":"10.1109\/CVPR.2015.7299094"},{"key":"38_CR22","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1109\/TPAMI.2016.2516982","volume":"38","author":"M Kristan","year":"2016","unstructured":"Kristan, M., et al.: A novel performance evaluation methodology for single-target trackers. IEEE Trans. Pattern Anal. Mach. Intell. 38, 2137 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Kristan, M., et al.: A novel performance evaluation methodology for single-target trackers. In: Proceedings of the European Conference on Computer Vision (2016)","DOI":"10.1109\/TPAMI.2016.2516982"},{"key":"38_CR24","unstructured":"Kristan, M., et al.: The visual object tracking vot2015 challenge results. In: International Conference on Computer Vision (2015)"},{"key":"38_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-16808-1_27","volume-title":"Computer Vision \u2013 ACCV 2014","author":"M Kristan","year":"2015","unstructured":"Kristan, M., Per\u0161, J., Suli\u010d, V., Kova\u010di\u010d, S.: A graphical model for rapid obstacle image-map estimation from unmanned surface vehicles. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 391\u2013406. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16808-1_27"},{"key":"38_CR26","unstructured":"Kwak, S., Nam, W., Han, B., Han, J.H.: Learning occlusion with likelihoods for visual tracking. In: International Conference on Computer Vision, pp. 1551\u20131558 (2011)"},{"key":"38_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/978-3-319-16181-5_18","volume-title":"Computer Vision - ECCV 2014 Workshops","author":"Y Li","year":"2015","unstructured":"Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254\u2013265. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16181-5_18"},{"key":"38_CR28","doi-asserted-by":"crossref","unstructured":"Luke\u017ei\u010d., Voj\u00ed\u0159, T., \u010cehovin Zajc, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: Computer Vision and Pattern Recognition, pp. 6309\u20136318 (2017)","DOI":"10.1109\/CVPR.2017.515"},{"issue":"8","key":"38_CR29","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s11263-018-1076-4","volume":"126","author":"C Ma","year":"2018","unstructured":"Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Adaptive correlation filters with long-term and short-term memory for object tracking. Int. J. Comput. Vis. 126(8), 771\u2013796 (2018)","journal-title":"Int. J. Comput. Vis."},{"key":"38_CR30","doi-asserted-by":"crossref","unstructured":"Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: Computer Vision and Pattern Recognition, pp. 5388\u20135396 (2015)","DOI":"10.1109\/CVPR.2015.7299177"},{"key":"38_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/978-3-642-41184-7_43","volume-title":"Image Analysis and Processing \u2013 ICIAP 2013","author":"ME Maresca","year":"2013","unstructured":"Maresca, M.E., Petrosino, A.: MATRIOSKA: a multi-level approach to fast tracking by learning. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 419\u2013428. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-41184-7_43"},{"key":"38_CR32","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":"38_CR33","doi-asserted-by":"crossref","unstructured":"Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Computer Vision and Pattern Recognition, pp. 4293\u20134302, June 2016","DOI":"10.1109\/CVPR.2016.465"},{"key":"38_CR34","doi-asserted-by":"crossref","unstructured":"Nebehay, G., Pflugfelder, R.: Clustering of static-adaptive correspondences for deformable object tracking. In: Computer Vision and Pattern Recognition, pp. 2784\u20132791 (2015)","DOI":"10.1109\/CVPR.2015.7298895"},{"issue":"12","key":"38_CR35","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/TPAMI.2013.250","volume":"36","author":"F Pernici","year":"2013","unstructured":"Pernici, F., Del Bimbo, A.: Object tracking by oversampling local features. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2538\u20132551 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR36","doi-asserted-by":"crossref","unstructured":"Tao, R., Gavves, E., Smeulders, A.W.M.: Siamese instance search for tracking. In: Computer Vision and Pattern Recognition, pp. 1420\u20131429 (2016)","DOI":"10.1109\/CVPR.2016.158"},{"key":"38_CR37","doi-asserted-by":"crossref","unstructured":"Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.S.: End-to-end representation learning for correlation filter based tracking. In: Computer Vision and Pattern Recognition, pp. 2805\u20132813 (2017)","DOI":"10.1109\/CVPR.2017.531"},{"key":"38_CR38","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: International Conference on Computer Vision, pp. 3119\u20133127, December 2015","DOI":"10.1109\/ICCV.2015.357"},{"issue":"7","key":"38_CR39","doi-asserted-by":"publisher","first-page":"1512","DOI":"10.1109\/TIP.2009.2019809","volume":"18","author":"J Weijer van de","year":"2009","unstructured":"van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512\u20131523 (2009)","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"38_CR40","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.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834\u20131848 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2018"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20890-5_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T02:45:45Z","timestamp":1663555545000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20890-5_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030208899","9783030208905"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20890-5_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"2 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2018.net\/","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"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"979","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"274","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"28% - 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"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}