{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:21:49Z","timestamp":1727065309521},"publisher-location":"Cham","reference-count":93,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030682378"},{"type":"electronic","value":"9783030682385"}],"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"},{"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_39","type":"book-chapter","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T07:02:43Z","timestamp":1611990163000},"page":"547-601","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":112,"title":["The Eighth Visual Object Tracking VOT2020 Challenge Results"],"prefix":"10.1007","author":[{"given":"Matej","family":"Kristan","sequence":"first","affiliation":[]},{"given":"Ale\u0161","family":"Leonardis","sequence":"additional","affiliation":[]},{"given":"Ji\u0159\u00ed","family":"Matas","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Felsberg","sequence":"additional","affiliation":[]},{"given":"Roman","family":"Pflugfelder","sequence":"additional","affiliation":[]},{"given":"Joni-Kristian","family":"K\u00e4m\u00e4r\u00e4inen","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Danelljan","sequence":"additional","affiliation":[]},{"given":"Luka \u010cehovin","family":"Zajc","sequence":"additional","affiliation":[]},{"given":"Alan","family":"Luke\u017ei\u010d","sequence":"additional","affiliation":[]},{"given":"Ondrej","family":"Drbohlav","sequence":"additional","affiliation":[]},{"given":"Linbo","family":"He","sequence":"additional","affiliation":[]},{"given":"Yushan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Song","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Jinyu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Gustavo","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Hauptmann","sequence":"additional","affiliation":[]},{"given":"Alireza","family":"Memarmoghadam","sequence":"additional","affiliation":[]},{"given":"\u00c1lvaro","family":"Garc\u00eda-Mart\u00edn","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Robinson","sequence":"additional","affiliation":[]},{"given":"Anton","family":"Varfolomieiev","sequence":"additional","affiliation":[]},{"given":"Awet Haileslassie","family":"Gebrehiwot","sequence":"additional","affiliation":[]},{"given":"Bedirhan","family":"Uzun","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Chi-Yi","family":"Tsai","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Micheloni","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Felix Jaremo","family":"Lawin","sequence":"additional","affiliation":[]},{"given":"Fredrik","family":"Gustafsson","sequence":"additional","affiliation":[]},{"given":"Gian Luca","family":"Foresti","sequence":"additional","affiliation":[]},{"given":"Goutam","family":"Bhat","sequence":"additional","affiliation":[]},{"given":"Guangqi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haibin","family":"Ling","sequence":"additional","affiliation":[]},{"given":"Haitao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hakan","family":"Cevikalp","sequence":"additional","affiliation":[]},{"given":"Haojie","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Haoran","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Hari Chandana","family":"Kuchibhotla","sequence":"additional","affiliation":[]},{"given":"Hasan","family":"Saribas","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Hossein","family":"Ghanei-Yakhdan","sequence":"additional","affiliation":[]},{"given":"Houqiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Houwen","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Huchuan","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Javad","family":"Khaghani","sequence":"additional","affiliation":[]},{"given":"Jesus","family":"Bescos","sequence":"additional","affiliation":[]},{"given":"Jianhua","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jianlong","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Jiaqian","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Jingtao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Josef","family":"Kittler","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Junhyun","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Kaicheng","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Kaiwen","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Kang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Kenan","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Li","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Linyuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Luc","family":"Van Gool","sequence":"additional","affiliation":[]},{"given":"Luca","family":"Bertinetto","sequence":"additional","affiliation":[]},{"given":"Matteo","family":"Dunnhofer","sequence":"additional","affiliation":[]},{"given":"Miao","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Mohana Murali","family":"Dasari","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Pengyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Philip H. S.","family":"Torr","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Radu","family":"Timofte","sequence":"additional","affiliation":[]},{"given":"Rama Krishna Sai","family":"Gorthi","sequence":"additional","affiliation":[]},{"given":"Seokeon","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Seyed Mojtaba","family":"Marvasti-Zadeh","sequence":"additional","affiliation":[]},{"given":"Shaochuan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Shohreh","family":"Kasaei","sequence":"additional","affiliation":[]},{"given":"Shoumeng","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Shuhao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Thomas B.","family":"Sch\u00f6n","sequence":"additional","affiliation":[]},{"given":"Tianyang","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Weiming","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Wengang","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Ke","sequence":"additional","affiliation":[]},{"given":"Xiao-Jun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiaolin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoyun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xuefeng","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yingjie","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Yingming","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yiwei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Yuezhou","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuncon","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Yunsung","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Yuzhang","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Zezhou","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhangyong","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Zhen-Hua","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Zhijun","family":"Mai","sequence":"additional","affiliation":[]},{"given":"Zhipeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhirong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Ziang","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,31]]},"reference":[{"issue":"8","key":"39_CR1","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","volume":"33","author":"B Babenko","year":"2011","unstructured":"Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619\u20131632 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"39_CR2","doi-asserted-by":"crossref","unstructured":"Berg, A., Ahlberg, J., Felsberg, M.: A thermal object tracking benchmark. In: 12th IEEE International Conference on Advanced Video- and Signal-based Surveillance, Karlsruhe, Germany, 25\u201328 August 2015. IEEE (2015)","DOI":"10.1109\/AVSS.2015.7301772"},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Berg, A., Johnander, J., de Gevigney, F.D., Ahlberg, J., Felsberg, M.: Semi-automatic annotation of objects in visual-thermal video. In: IEEE International Conference on Computer Vision, ICCV Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00277"},{"key":"39_CR4","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":"39_CR5","doi-asserted-by":"crossref","unstructured":"Bhat, G., Danelljan, M., Gool, L.V., Timofte, R.: Learning discriminative model prediction for tracking. In: IEEE International Conference on Computer Vision, ICCV (2019)","DOI":"10.1109\/ICCV.2019.00628"},{"key":"39_CR6","doi-asserted-by":"crossref","unstructured":"Bhat, G., Johnander, J., Danelljan, M., Khan, F.S., Felsberg, M.: Unveiling the power of deep tracking. In: ECCV, pp. 483\u2013498 (2018)","DOI":"10.1007\/978-3-030-01216-8_30"},{"key":"39_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1007\/978-3-030-58536-5_46","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Bhat","year":"2020","unstructured":"Bhat, G., et al.: Learning what to learn for video object segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 777\u2013794. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_46"},{"key":"39_CR8","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: Hybrid task cascade for instance segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00511"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"39_CR11","doi-asserted-by":"crossref","unstructured":"Dai, K., Zhang, Y., Wang, D., Li, J., Lu, H., Yang, X.: High-performance long-term tracking with meta-updater. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00633"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ECO: efficient convolution operators for tracking. In: CVPR, pp. 6638\u20136646 (2017)","DOI":"10.1109\/CVPR.2017.733"},{"key":"39_CR13","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ATOM: accurate tracking by overlap maximization. In: CVPR, pp. 4660\u20134669 (2019)","DOI":"10.1109\/CVPR.2019.00479"},{"key":"39_CR14","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Gool, L.V., Timofte, R.: Probabilistic regression for visual tracking. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00721"},{"issue":"8","key":"39_CR15","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","volume":"39","author":"M Danelljan","year":"2016","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 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Dunnhofer, M., Martinel, N., Luca Foresti, G., Micheloni, C.: Visual tracking by means of deep reinforcement learning and an expert demonstrator. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, October 2019","DOI":"10.1109\/ICCVW.2019.00282"},{"key":"39_CR17","unstructured":"Dunnhofer, M., Martinel, N., Micheloni, C.: A distilled model for tracking and tracker fusion (2020)"},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Fan, H., et al.: Lasot: a high-quality benchmark for large-scale single object tracking. In: Computer Vision Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00552"},{"key":"39_CR19","doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: Need for speed: a benchmark for higher frame rate object tracking. CoRR abs\/1703.05884 (2017). http:\/\/arxiv.org\/abs\/1703.05884","DOI":"10.1109\/ICCV.2017.128"},{"key":"39_CR20","doi-asserted-by":"crossref","unstructured":"Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: a new change detection benchmark dataset. In: CVPR Workshops, pp. 1\u20138. IEEE (2012)","DOI":"10.1109\/CVPRW.2012.6238919"},{"issue":"1","key":"39_CR21","first-page":"185","volume":"19","author":"C Guo","year":"2009","unstructured":"Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185\u2013198 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"39_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-030-58565-5_20","volume-title":"Computer Vision \u2013 ECCV 2020","author":"FK Gustafsson","year":"2020","unstructured":"Gustafsson, F.K., Danelljan, M., Bhat, G., Sch\u00f6n, T.B.: Energy-based models for deep probabilistic regression. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 325\u2013343. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_20"},{"key":"39_CR23","unstructured":"Gustafsson, F.K., Danelljan, M., Timofte, R., Sch\u00f6n, T.B.: How to train your energy-based model for regression. CoRR abs\/2005.01698 (2020). https:\/\/arxiv.org\/abs\/2005.01698"},{"issue":"3","key":"39_CR24","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"J Henriques","year":"2015","unstructured":"Henriques, J., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. PAMI 37(3), 583\u2013596 (2015)","journal-title":"PAMI"},{"key":"39_CR25","unstructured":"Huang, L., Zhao, X., Huang, K.: Got-10k: a large high-diversity benchmark for generic object tracking in the wild. arXiv:1810.11981 (2018)"},{"key":"39_CR26","doi-asserted-by":"crossref","unstructured":"Huang, L., Zhao, X., Huang, K.: GlobalTrack: a simple and strong baseline for long-term tracking. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6758"},{"key":"39_CR27","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: alexnet-level accuracy with 50x fewer parameters and $$<$$0.5mb model size. arXiv:1602.07360 (2016)"},{"key":"39_CR28","unstructured":"Jack, V., et al.: Long-term tracking in the wild: A benchmark. arXiv:1803.09502 (2018)"},{"key":"39_CR29","doi-asserted-by":"crossref","unstructured":"Jung, I., Son, J., Baek, M., Han, B.: Real-time MDNet. In: ECCV, pp. 83\u201398 (2018)","DOI":"10.1007\/978-3-030-01225-0_6"},{"issue":"7","key":"39_CR30","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. (TPAMI) 34(7), 1409\u20131422 (2012). https:\/\/doi.org\/10.1109\/TPAMI.2011.239","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"39_CR31","unstructured":"Kristan, M., et al.: The seventh visual object tracking vot2019 challenge results. In: ICCV2019 Workshops, Workshop on Visual Object Tracking Challenge (2019)"},{"key":"39_CR32","unstructured":"Kristan, M., et al.: The visual object tracking vot2018 challenge results. In: ECCV2018 Workshops, Workshop on Visual Object Tracking Challenge (2018)"},{"key":"39_CR33","unstructured":"Kristan, M., et al.: The visual object tracking vot2017 challenge results. In: ICCV2017 Workshops, Workshop on Visual Object Tracking Challenge (2017)"},{"key":"39_CR34","unstructured":"Kristan, M., et al.: The visual object tracking vot2016 challenge results. In: ECCV2016 Workshops, Workshop on Visual Object Tracking Challenge (2016)"},{"key":"39_CR35","unstructured":"Kristan, M., et al.: The visual object tracking vot2015 challenge results. In: ICCV2015 Workshops, Workshop on Visual Object Tracking Challenge (2015)"},{"key":"39_CR36","unstructured":"Kristan, M., et al.: The visual object tracking vot2013 challenge results. In: ICCV2013 Workshops, Workshop on Visual Object Tracking Challenge, pp. 98\u2013111 (2013)"},{"key":"39_CR37","unstructured":"Kristan, M., et al.: The visual object tracking vot2014 challenge results. In: ECCV2014 Workshops, Workshop on Visual Object Tracking Challenge (2014)"},{"issue":"11","key":"39_CR38","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(11), 2137\u20132155 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"39_CR39","unstructured":"Leal-Taix\u00e9, L., Milan, A., Reid, I.D., Roth, S., Schindler, K.: Motchallenge 2015: towards a benchmark for multi-target tracking. CoRR abs\/1504.01942 (2015). http:\/\/arxiv.org\/abs\/1504.01942"},{"key":"39_CR40","doi-asserted-by":"crossref","unstructured":"Li, A., Li, M., Wu, Y., Yang, M.H., Yan, S.: Nus-pro: a new visual tracking challenge. IEEE-PAMI (2015)","DOI":"10.1109\/TPAMI.2015.2417577"},{"key":"39_CR41","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":"39_CR42","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: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8971\u20138980, June 2018","DOI":"10.1109\/CVPR.2018.00935"},{"key":"39_CR43","doi-asserted-by":"crossref","unstructured":"Li, C., Liang, X., Lu, Y., Zhao, N., Tang, J.: RGB-T object tracking: benchmark and baseline. Pattern Recogn. (2019, submitted)","DOI":"10.1016\/j.patcog.2019.106977"},{"issue":"12","key":"39_CR44","doi-asserted-by":"publisher","first-page":"5630","DOI":"10.1109\/TIP.2015.2482905","volume":"24","author":"P Liang","year":"2015","unstructured":"Liang, P., Blasch, E., Ling, H.: Encoding color information for visual tracking: algorithms and benchmark. IEEE Trans. Image Process. 24(12), 5630\u20135644 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"39_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"39_CR46","doi-asserted-by":"crossref","unstructured":"Luke\u017ei\u010d, A., Kart, U., K\u00e4m\u00e4r\u00e4inen, J., Matas, J., Kristan, M.: CDTB: a color and depth visual object tracking dataset and benchmark. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.01011"},{"key":"39_CR47","doi-asserted-by":"crossref","unstructured":"Luke\u017ei\u010d, A., Voj\u00edr\u0303 T., \u010cehovin Zajc, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6309\u20136318, July 2017","DOI":"10.1109\/CVPR.2017.515"},{"key":"39_CR48","unstructured":"Luke\u017ei\u010d, A., \u010cehovin Zajc, L., Voj\u00edr\u0303 T., Matas, J., Kristan, M.: Now you see me: evaluating performance in long-term visual tracking. CoRR abs\/1804.07056 (2018). http:\/\/arxiv.org\/abs\/1804.07056"},{"key":"39_CR49","doi-asserted-by":"crossref","unstructured":"Lukezic, A., Cehovin Zajc, L., Vojir, T., Matas, J., Kristan, M.: Performance evaluation methodology for long-term single object tracking. IEEE Trans. Cybern. (2020)","DOI":"10.1109\/TCYB.2020.2980618"},{"key":"39_CR50","doi-asserted-by":"crossref","unstructured":"Lukezic, A., Matas, J., Kristan, M.: D3S - a discriminative single shot segmentation tracker. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00716"},{"key":"39_CR51","doi-asserted-by":"publisher","unstructured":"Memarmoghadam, A., Moallem, P.: Size-aware visual object tracking via dynamic fusion of correlation filter-based part regressors. Signal Process. 164, 84\u201398 (2019). https:\/\/doi.org\/10.1016\/j.sigpro.2019.05.021. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0165168419301872","DOI":"10.1016\/j.sigpro.2019.05.021"},{"key":"39_CR52","unstructured":"Moudgil, A., Gandhi, V.: Long-term visual object tracking benchmark. arXiv preprint arXiv:1712.01358 (2017)"},{"key":"39_CR53","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":"39_CR54","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: ECCV, pp. 300\u2013317 (2018)","DOI":"10.1007\/978-3-030-01246-5_19"},{"key":"39_CR55","doi-asserted-by":"crossref","unstructured":"Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: CVPR, pp. 4293\u20134302 (2016)","DOI":"10.1109\/CVPR.2016.465"},{"key":"39_CR56","doi-asserted-by":"crossref","unstructured":"Oh, S.W., Lee, J.Y., Xu, N., Kim, S.J.: Video object segmentation using space-time memory networks. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00932"},{"issue":"12","key":"39_CR57","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). https:\/\/doi.org\/10.1109\/TPAMI.2013.250","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"39_CR58","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1109\/34.879790","volume":"22","author":"PJ Phillips","year":"2000","unstructured":"Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090\u20131104 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"39_CR59","doi-asserted-by":"crossref","unstructured":"Real, E., Shlens, J., Mazzocchi, S., Pan, X., Vanhoucke, V.: YouTube-BoundingBoxes: a large high-precision human-annotated data set for object detection in video. In: Computer Vision and Pattern Recognition, pp. 7464\u20137473 (2017)","DOI":"10.1109\/CVPR.2017.789"},{"key":"39_CR60","doi-asserted-by":"crossref","unstructured":"Robinson, A., Lawin, F.J., Danelljan, M., Khan, F.S., Felsberg, M.: Learning fast and robust target models for video object segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Computer Vision Foundation, June 2020","DOI":"10.1109\/CVPR42600.2020.00743"},{"key":"39_CR61","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"1\u20133","key":"39_CR62","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","volume":"77","author":"DA Ross","year":"2008","unstructured":"Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1\u20133), 125\u2013141 (2008)","journal-title":"Int. J. Comput. Vis."},{"issue":"3","key":"39_CR63","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"IJCV"},{"key":"39_CR64","unstructured":"Seoung, W.O., Lee, J.Y., Kim, S.J.: Fast video object segmentation by reference-guided mask propagation. In: Computer Vision Pattern Recognition, pp. 7376\u20137385 (2018)"},{"key":"39_CR65","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.230","author":"AWM Smeulders","year":"2013","unstructured":"Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. TPAMI (2013). https:\/\/doi.org\/10.1109\/TPAMI.2013.230","journal-title":"TPAMI"},{"key":"39_CR66","doi-asserted-by":"crossref","unstructured":"Solera, F., Calderara, S., Cucchiara, R.: Towards the evaluation of reproducible robustness in tracking-by-detection. In: Advanced Video and Signal Based Surveillance, pp. 1\u20136 (2015)","DOI":"10.1109\/AVSS.2015.7301755"},{"key":"39_CR67","doi-asserted-by":"crossref","unstructured":"Song, S., Xiao, J.: Tracking revisited using RGBD camera: unified benchmark and baselines. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.36"},{"key":"39_CR68","unstructured":"Tao, R., Gavves, E., Smeulders, A.W.M.: Tracking for half an hour. CoRR abs\/1711.10217 (2017). http:\/\/arxiv.org\/abs\/1711.10217"},{"key":"39_CR69","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. arXiv preprint arXiv:1904.01355 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"39_CR70","doi-asserted-by":"crossref","unstructured":"\u010cehovin, L., Kristan, M., Leonardis, A.: Is my new tracker really better than yours? Technical report 10, ViCoS Lab, University of Ljubljana, October 2013. http:\/\/prints.vicos.si\/publications\/302","DOI":"10.1109\/WACV.2014.6836055"},{"key":"39_CR71","doi-asserted-by":"publisher","unstructured":"\u010cehovin, L.: TraX: The visual Tracking eXchange Protocol and Library. Neurocomputing (2017). https:\/\/doi.org\/10.1016\/j.neucom.2017.02.036","DOI":"10.1016\/j.neucom.2017.02.036"},{"issue":"3","key":"39_CR72","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/TIP.2016.2520370","volume":"25","author":"L \u010cehovin","year":"2016","unstructured":"\u010cehovin, L., Leonardis, A., Kristan, M.: Visual object tracking performance measures revisited. IEEE Trans. Image Process. 25(3), 1261\u20131274 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"39_CR73","doi-asserted-by":"crossref","unstructured":"Voj\u00edr\u0303, T., Noskova, J., Matas, J.: Robust scale-adaptive mean-shift for tracking. Pattern Recogn. Lett. 49, 250\u2013258 (2014)","DOI":"10.1016\/j.patrec.2014.03.025"},{"key":"39_CR74","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: CVPR, pp. 1328\u20131338 (2019)","DOI":"10.1109\/CVPR.2019.00142"},{"key":"39_CR75","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L.: SOLO: segmenting objects by locations. arXiv preprint arXiv:1912.04488 (2019)","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"39_CR76","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Computer Vision Pattern Recognition (2013)","DOI":"10.1109\/CVPR.2013.312"},{"issue":"9","key":"39_CR77","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. PAMI 37(9), 1834\u20131848 (2015)","journal-title":"PAMI"},{"key":"39_CR78","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1109\/TCYB.2017.2740952","volume":"48","author":"J Xiao","year":"2018","unstructured":"Xiao, J., Stolkin, R., Gao, Y., Leonardis, A.: Robust fusion of color and depth data for RGB-D target tracking using adaptive range-invariant depth models and spatio-temporal consistency constraints. IEEE Trans. Cybern. 48, 2485\u20132499 (2018)","journal-title":"IEEE Trans. Cybern."},{"key":"39_CR79","doi-asserted-by":"crossref","unstructured":"Xu, N., Price, B., Yang, J., Huang, T.: Deep grabcut for object selection. In: Proceedings of British Machine Vision Conference (2017)","DOI":"10.5244\/C.31.182"},{"key":"39_CR80","unstructured":"Xu, T., Feng, Z.H., Wu, X.J., Kittler, J.: AFAT: adaptive failure-aware tracker for robust visual object tracking. arXiv preprint arXiv:2005.13708 (2020)"},{"key":"39_CR81","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wang, Z., Li, Z., Ye, Y., Yu, G.: SiamFC++: towards robust and accurate visual tracking with target estimation guidelines. arXiv preprint arXiv:1911.06188 (2019)","DOI":"10.1609\/aaai.v34i07.6944"},{"key":"39_CR82","doi-asserted-by":"crossref","unstructured":"Yan, B., Wang, D., Lu, H., Yang, X.: Alpha-refine: boosting tracking performance by precise bounding box estimation. arXiv preprint arXiv:2007.02024 (2020)","DOI":"10.1109\/CVPR46437.2021.00525"},{"key":"39_CR83","doi-asserted-by":"crossref","unstructured":"Yan, B., Zhao, H., Wang, D., Lu, H., Yang, X.: Skimming-Perusal Tracking: a framework for real-time and robust long-term tracking. In: IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00247"},{"key":"39_CR84","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.: RepPoints: point set representation for object detection. In: The IEEE International Conference on Computer Vision (ICCV), pp. 9657\u20139666, October 2019","DOI":"10.1109\/ICCV.2019.00975"},{"key":"39_CR85","unstructured":"Yiming, L., Shen, J., Pantic, M.: Mobile face tracking: a survey and benchmark. arXiv:1805.09749v1 (2018)"},{"key":"39_CR86","unstructured":"Young, D.P., Ferryman, J.M.: PETS Metrics: on-line performance evaluation service. In: Proceedings of the 14th International Conference on Computer Communications and Networks, ICCCN 2005, pp. 317\u2013324 (2005)"},{"key":"39_CR87","doi-asserted-by":"crossref","unstructured":"Zhang, L., Danelljan, M., Gonzalez-Garcia, A., van de Weijer, J., Khan, F.S.: Multi-modal fusion for end-to-end RGB-T tracking. In: IEEE International Conference on Computer Vision, ICCV Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00278"},{"key":"39_CR88","unstructured":"Zhang, P., Zhao, J., Wang, D., Lu, H., Yang, X.: Jointly modeling motion and appearance cues for robust RGB-T tracking. CoRR abs\/2007.02041 (2020)"},{"key":"39_CR89","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wu, Z., Peng, H., Lin, S.: A transductive approach for video object segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4000\u20134009, June 2020","DOI":"10.1109\/CVPR42600.2020.00698"},{"key":"39_CR90","unstructured":"Zhang, Y., Wang, D., Wang, L., Qi, J., Lu, H.: Learning regression and verification networks for long-term visual tracking. CoRR abs\/1809.04320 (2018)"},{"key":"39_CR91","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Peng, H.: Deeper and wider Siamese networks for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4591\u20134600, June 2019","DOI":"10.1109\/CVPR.2019.00472"},{"key":"39_CR92","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Peng, H., Fu, J., Li, B., Hu, W.: Ocean: object-aware anchor-free tracking. arXiv preprint arXiv:2006.10721 (2020)","DOI":"10.1007\/978-3-030-58589-1_46"},{"key":"39_CR93","unstructured":"Zhu, P., Wen, L., Bian, X., Haibin, L., Hu, Q.: Vision meets drones: a challenge. arXiv preprint arXiv:1804.07437 (2018)"}],"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_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T18:49:22Z","timestamp":1697741362000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-68238-5_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030682378","9783030682385"],"references-count":93,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68238-5_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}