{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:45:24Z","timestamp":1785955524488,"version":"3.56.0"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030682378","type":"print"},{"value":"9783030682385","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-68238-5_22","type":"book-chapter","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T07:02:43Z","timestamp":1611990163000},"page":"299-314","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Hard Occlusions in Visual Object Tracking"],"prefix":"10.1007","author":[{"given":"Thijs P.","family":"Kuipers","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devanshu","family":"Arya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak K.","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,31]]},"reference":[{"issue":"7","key":"22_CR1","first-page":"1442","volume":"36","author":"AW Smeulders","year":"2013","unstructured":"Smeulders, A.W., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442\u20131468 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2014.2370854","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, 1 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR3","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":"22_CR4","unstructured":"Kristan, M., et al.: The seventh visual object tracking vot2019 challenge results (2019)"},{"key":"22_CR5","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. (2019)"},{"key":"22_CR6","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 Conference on Computer Vision and Pattern Recognition, pp. 5374\u20135383 (2019)","DOI":"10.1109\/CVPR.2019.00552"},{"key":"22_CR7","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":"22_CR8","doi-asserted-by":"crossref","unstructured":"Noh, J., Lee, S., Kim, B., Kim, G.: Improving occlusion and hard negative handling for single-stage pedestrian detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 966\u2013974 (2018)","DOI":"10.1109\/CVPR.2018.00107"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4310\u20134318 (2015)","DOI":"10.1109\/ICCV.2015.490"},{"key":"22_CR10","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: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544\u20132550. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters (2010)","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Shahbaz Khan, F., Felsberg, M., Van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090\u20131097 (2014)","DOI":"10.1109\/CVPR.2014.143"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Huang, Y., Essa, I.: Tracking multiple objects through occlusions. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 1051\u20131058. IEEE (2005)","DOI":"10.1109\/CVPR.2005.350"},{"issue":"3","key":"22_CR14","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2014","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 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR15","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":"22_CR16","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, September 1\u20135, 2014. BMVA Press (2014)","DOI":"10.5244\/C.28.65"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Kiani Galoogahi, H., Sim, T., Lucey, S.: Correlation filters with limited boundaries. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4630\u20134638 (2015)","DOI":"10.1109\/CVPR.2015.7299094"},{"key":"22_CR18","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":"22_CR19","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3074\u20133082 (2015)","DOI":"10.1109\/ICCV.2015.352"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: ECO: efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6638\u20136646 (2017)","DOI":"10.1109\/CVPR.2017.733"},{"key":"22_CR21","unstructured":"Gan, Q., Guo, Q., Zhang, Z., Cho, K.: First step toward model-free, anonymous object tracking with recurrent neural networks. arXiv preprint arXiv:1511.06425 (2015)"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Kahou, S.E., Michalski, V., Memisevic, R., Pal, C., Vincent, P.: RATM: recurrent attentive tracking model. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1613\u20131622. IEEE (2017)","DOI":"10.1109\/CVPRW.2017.206"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293\u20134302 (2016)","DOI":"10.1109\/CVPR.2016.465"},{"key":"22_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/978-3-319-46448-0_45","volume-title":"Computer Vision \u2013 ECCV 2016","author":"D Held","year":"2016","unstructured":"Held, D., Thrun, S., Savarese, S.: Learning to track at 100 FPS with deep regression networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 749\u2013765. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_45"},{"key":"22_CR25","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":"22_CR26","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":"22_CR27","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 Conference on Computer Vision and Pattern Recognition, pp. 4282\u20134291 (2019)","DOI":"10.1109\/CVPR.2019.00441"},{"key":"22_CR28","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 Conference on Computer Vision and Pattern Recognition, pp. 4660\u20134669 (2019)","DOI":"10.1109\/CVPR.2019.00479"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Bhat, G., Danelljan, M., Gool, L.V., Timofte, R.: Learning discriminative model prediction for tracking (2019)","DOI":"10.1109\/ICCV.2019.00628"},{"issue":"11","key":"22_CR30","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1109\/TPAMI.2004.96","volume":"26","author":"A Yilmaz","year":"2004","unstructured":"Yilmaz, A., Li, X., Shah, M.: Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1531\u20131536 (2004)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR31","unstructured":"Gupta, D.K., Gavves, E., Smeulders, A.W.: Tackling occlusion in Siamese tracking with structured dropouts. arXiv:2006.16571 (2020)"},{"key":"22_CR32","doi-asserted-by":"crossref","unstructured":"Pan, J., Hu, B.: Robust occlusion handling in object tracking. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2007)","DOI":"10.1109\/CVPR.2007.383453"},{"key":"22_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tao, H.: A background layer model for object tracking through occlusion. In: Proceedings Ninth IEEE International Conference on Computer Vision, pp. 1079\u20131085. IEEE (2003)","DOI":"10.1109\/ICCV.2003.1238469"},{"key":"22_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/3-540-57956-7_22","volume-title":"Computer Vision\u2014ECCV 1994","author":"D Koller","year":"1994","unstructured":"Koller, D., Weber, J., Malik, J.: Robust multiple car tracking with occlusion reasoning. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 189\u2013196. Springer, Heidelberg (1994). https:\/\/doi.org\/10.1007\/3-540-57956-7_22"},{"key":"22_CR35","doi-asserted-by":"crossref","unstructured":"Lee, B.Y., Liew, L.H., Cheah, W.S., Wang, Y.C.: Occlusion handling in videos object tracking: a survey. In: IOP Conference Series: Earth and Environmental Science, vol. 18, p. 012020. IOP Publishing (2014)","DOI":"10.1088\/1755-1315\/18\/1\/012020"},{"issue":"3","key":"22_CR36","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.imavis.2006.12.007","volume":"26","author":"D Greenhill","year":"2008","unstructured":"Greenhill, D., Renno, J., Orwell, J., Jones, G.A.: Occlusion analysis: learning and utilising depth maps in object tracking. Image Vis. Comput. 26(3), 430\u2013441 (2008)","journal-title":"Image Vis. Comput."},{"key":"22_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/978-3-642-17289-2_43","volume-title":"Advances in Visual Computing","author":"Y Ma","year":"2010","unstructured":"Ma, Y., Chen, Q.: Depth assisted occlusion handling in video object tracking. In: Bebis, G., et al. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 449\u2013460. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-17289-2_43"},{"key":"22_CR38","doi-asserted-by":"crossref","unstructured":"Ali, A., Terada, K.: A framework for human tracking using Kalman filter and fast mean shift algorithms. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1028\u20131033. IEEE (2009)","DOI":"10.1109\/ICCVW.2009.5457591"},{"key":"22_CR39","doi-asserted-by":"crossref","unstructured":"Zhao, J., Qiao, W., Men, G.Z.: An approach based on mean shift and Kalman filter for target tracking under occlusion. In: 2009 International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2058\u20132062. IEEE (2009)","DOI":"10.1109\/ICMLC.2009.5212129"},{"key":"22_CR40","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"},{"key":"22_CR41","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"22_CR42","unstructured":"Kristan, M., et al.: The sixth visual object tracking vot2018 challenge results. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)"},{"key":"22_CR43","unstructured":"Moudgil, A., Gandhi, V.: Long-term visual object tracking benchmark. arXiv preprint arXiv:1712.01358 (2017)"},{"key":"22_CR44","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., Yang, M.: Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411\u20132418 (2013)","DOI":"10.1109\/CVPR.2013.312"},{"key":"22_CR45","unstructured":"Gavves, E., Tao, R., Gupta, D.K., Smeulders, A.W.M.: Model decay in long-term tracking. arXiv:1908.01603 (2019)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68238-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T23:05:08Z","timestamp":1738191908000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-68238-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030682378","9783030682385"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68238-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"31 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}