{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T01:55:02Z","timestamp":1783562102348,"version":"3.55.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T00:00:00Z","timestamp":1515369600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1007\/s11263-017-1061-3","type":"journal-article","created":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T04:21:06Z","timestamp":1515385266000},"page":"671-688","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":358,"title":["Discriminative Correlation Filter Tracker with Channel and Spatial Reliability"],"prefix":"10.1007","volume":"126","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6316-2707","authenticated-orcid":false,"given":"Alan","family":"Luke\u017ei\u010d","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom\u00e1\u0161","family":"Voj\u00ed\u0159","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luka","family":"\u010cehovin\u00a0Zajc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji\u0159\u00ed","family":"Matas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matej","family":"Kristan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,1,8]]},"reference":[{"issue":"8","key":"1061_CR1","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","volume":"33","author":"B Babenko","year":"2011","unstructured":"Babenko, B., Yang, M. H., & Belongie, S. (2011). Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1619\u20131632.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1061_CR2","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., & Torr, P. H. S. (2016a). Staple: Complementary learners for real-time tracking. In Computer vision and pattern recognition (pp. 1401\u20131409).","DOI":"10.1109\/CVPR.2016.156"},{"key":"1061_CR3","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. (2016b). Fully-convolutional siamese networks for object tracking. arXiv preprint arXiv:1606.09549.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"1061_CR4","doi-asserted-by":"crossref","unstructured":"Bolme, D. S., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010). Visual object tracking using adaptive correlation filters. In IEEE: Computer vision and pattern recognition (pp. 2544\u20132550).","DOI":"10.1109\/CVPR.2010.5539960"},{"issue":"1","key":"1061_CR5","doi-asserted-by":"crossref","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. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1), 1\u2013122.","journal-title":"Foundations and Trends in Machine Learning"},{"issue":"3","key":"1061_CR6","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. (2016). Visual object tracking performance measures revisited. IEEE Transactions on Image Processing, 25(3), 1261\u20131274.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1061_CR7","first-page":"886","volume":"1","author":"N Dalal","year":"2005","unstructured":"Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. Computer Vision and Pattern Recognition, 1, 886\u2013893.","journal-title":"Computer Vision and Pattern Recognition"},{"key":"1061_CR8","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F. S., & Felsberg, M. (2014a). Accurate scale estimation for robust visual tracking. In Proceedings of British machine vision conference (pp. 1\u201311).","DOI":"10.5244\/C.28.65"},{"key":"1061_CR9","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F. S., & Felsberg, M. (2015a). Convolutional features for correlation filter based visual tracking. In IEEE international conference on computer vision workshop (ICCVW) (pp. 621\u2013629).","DOI":"10.1109\/ICCVW.2015.84"},{"key":"1061_CR10","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2015b). Learning spatially regularized correlation filters for visual tracking. In International conference on computer vision (pp. 4310\u20134318).","DOI":"10.1109\/ICCV.2015.490"},{"issue":"8","key":"1061_CR11","doi-asserted-by":"crossref","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. (2017). Discriminative scale space tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(8), 1561\u20131575.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1061_CR12","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F. S., Felsberg, M., & van\u00a0de Weijer, J. (2014b). Adaptive color attributes for real-time visual tracking. In 2014 IEEE conference on computer vision and pattern recognition, CVPR 2014, Columbus, OH, USA, June 23\u201328, 2014 (pp. 1090\u20131097).","DOI":"10.1109\/CVPR.2014.143"},{"key":"1061_CR13","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Robinson, A., Khan, F. S., & Felsberg, M. (2016). Beyond correlation filters: Learning continuous convolution operators for visual tracking. In Proceedings of the European conference on computer vision (pp. 472\u2013488). Springer.","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"1061_CR14","doi-asserted-by":"crossref","unstructured":"Dinh, T. B., Vo, N., & Medioni, G. (2011). Context tracker: Exploring supporters and distracters in unconstrained environments. In Computer vision and pattern recognition (pp. 1177\u20131184).","DOI":"10.1109\/CVPR.2011.5995733"},{"issue":"3","key":"1061_CR15","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1109\/TNN.2007.891190","volume":"18","author":"A Diplaros","year":"2007","unstructured":"Diplaros, A., Vlassis, N., & Gevers, T. (2007). A spatially constrained generative model and an em algorithm for image segmentation. IEEE Transactions on Neural Networks, 18(3), 798\u2013808.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"9","key":"1061_CR16","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"P Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P., Girshick, R., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627\u20131645.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1061_CR17","doi-asserted-by":"crossref","unstructured":"Galoogahi, H. K., Sim, T., & Lucey, S. (2013). Multi-channel correlation filters. In International conference on computer vision (pp. 3072\u20133079).","DOI":"10.1109\/ICCV.2013.381"},{"key":"1061_CR18","first-page":"47","volume":"1","author":"H Grabner","year":"2006","unstructured":"Grabner, H., Grabner, M., & Bischof, H. (2006). Real-time tracking via on-line boosting. Proceedings of British Machine Vision Conference, 1, 47\u201356.","journal-title":"Proceedings of British Machine Vision Conference"},{"key":"1061_CR19","doi-asserted-by":"crossref","unstructured":"Hare, S., Saffari, A., & Torr, P. H. S. (2011). Struck: Structured output tracking with kernels. In International conference on computer vision, IEEE Computer Society, Washington, DC, USA (pp. 263\u2013270).","DOI":"10.1109\/ICCV.2011.6126251"},{"key":"1061_CR20","doi-asserted-by":"crossref","unstructured":"Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2012). Exploiting the circulant structure of tracking-by-detection with kernels. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds.), Proceedings of European conference computer vision (pp. 702\u2013715). Berlin: Springer.","DOI":"10.1007\/978-3-642-33765-9_50"},{"issue":"3","key":"1061_CR21","doi-asserted-by":"crossref","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. (2015). High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 583\u2013596.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"11","key":"1061_CR22","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1364\/AO.19.001758","volume":"19","author":"CF Hester","year":"1980","unstructured":"Hester, C. F., & Casasent, D. (1980). Multivariant technique for multiclass pattern recognition. Applied Optics, 19(11), 1758\u20131761.","journal-title":"Applied Optics"},{"key":"1061_CR23","doi-asserted-by":"crossref","unstructured":"Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., & Tao, D. (2015). Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking. In Computer vision and pattern recognition (pp. 749\u2013758).","DOI":"10.1109\/CVPR.2015.7298675"},{"key":"1061_CR24","unstructured":"Jia, X., Lu, H., & Yang, M. H. (2012). Visual tracking via adaptive structural local sparse appearance model. In Computer vision and pattern recognition (pp. 1822\u20131829)."},{"issue":"7","key":"1061_CR25","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/TPAMI.2011.239","volume":"34","author":"Z Kalal","year":"2012","unstructured":"Kalal, Z., Mikolajczyk, K., & Matas, J. (2012). Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7), 1409\u20131422.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1061_CR26","unstructured":"Kiani\u00a0Galoogahi, H., Sim, T., Lucey, S. (2015). Correlation filters with limited boundaries. In Computer vision and pattern recognition (pp. 4630\u20134638)."},{"issue":"3","key":"1061_CR27","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/TCYB.2015.2412251","volume":"46","author":"M Kristan","year":"2016","unstructured":"Kristan, M., Kenk, V. S., Kova\u010di\u010d, S., & Per\u0161, J. (2016a). Fast image-based obstacle detection from unmanned surface vehicles. IEEE Transactions on Cybernetics, 46(3), 641\u2013654.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"1061_CR28","doi-asserted-by":"crossref","unstructured":"Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., \u010cehovin, L., et\u00a0al. (2016b). The visual object tracking vot2016 challenge results. In Proceedings of the European conference on computer vision.","DOI":"10.1007\/978-3-319-48881-3_54"},{"key":"1061_CR29","unstructured":"Kristan, M., Matas, J., Leonardis, A., Felsberg, M., \u010cehovin, L., Fernandez, G., et\u00a0al. (2015). The visual object tracking vot2015 challenge results. In International conference on computer vision"},{"key":"1061_CR30","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1109\/TPAMI.2016.2516982","volume":"38","author":"M Kristan","year":"2016","unstructured":"Kristan, M., Matas, J., Leonardis, A., Vojir, T., Pflugfelder, R., Fernandez, G., et al. (2016c). A novel performance evaluation methodology for single-target trackers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2137\u20132155.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1061_CR31","unstructured":"Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., \u010cehovin, L., Nebehay, G., et\u00a0al. (2014). The visual object tracking vot2014 challenge results. In Proceedings of the European conference on computer vision (pp. 191\u2013217)."},{"key":"1061_CR32","doi-asserted-by":"crossref","unstructured":"Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Porikli, F., \u010cehovinehovin, L., et al. (2013). The visual object tracking vot2013 challenge results. In The visual object tracking challenge VOT2013, in conjunction with ICCV2013 (pp. 98\u2013111).","DOI":"10.1109\/ICCVW.2013.20"},{"key":"1061_CR33","unstructured":"Li, Y., & Zhu, J. (2014). A scale adaptive kernel correlation filter tracker with feature integration. In Proceedings of the European conference on computer vision (pp. 254\u2013265)."},{"issue":"12","key":"1061_CR34","doi-asserted-by":"crossref","first-page":"5630","DOI":"10.1109\/TIP.2015.2482905","volume":"24","author":"P Liang","year":"2015","unstructured":"Liang, P., Blasch, E., & Ling, H. (2015). Encoding color information for visual tracking: Algorithms and benchmark. IEEE Transactions on Image Processing, 24(12), 5630\u20135644.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1061_CR35","doi-asserted-by":"crossref","unstructured":"Liu, B., Huang, J., Yang, L., & Kulikowsk, C. (2011). Robust tracking using local sparse appearance model and k-selection. In Computer vision and pattern recognition (pp. 1313\u20131320).","DOI":"10.1109\/CVPR.2011.5995730"},{"key":"1061_CR36","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, T., Cao, X., & Xu, C. (2016). Structural correlation filter for robust visual tracking. In Computer vision and pattern recognition (pp. 4312\u20134320).","DOI":"10.1109\/CVPR.2016.467"},{"key":"1061_CR37","doi-asserted-by":"crossref","unstructured":"Liu, T., Wang, G., & Yang, Q. (2015). Real-time part-based visual tracking via adaptive correlation filters. In Computer vision and pattern recognition (pp. 4902\u20134912).","DOI":"10.1109\/CVPR.2015.7299124"},{"key":"1061_CR38","first-page":"1","volume":"99","author":"A Luke\u017ei\u010d","year":"2017","unstructured":"Luke\u017ei\u010d, A., Zajc, L. \u010c., & Kristan, M. (2017). Deformable parts correlation filters for robust visual tracking. IEEE Transactions on Cybernetics, 99, 1\u201313.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"1061_CR39","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J. B., Yang, X., & Yang, M. H. (2015). Hierarchical convolutional features for visual tracking. In International conference on computer vision (pp 3074\u20133082).","DOI":"10.1109\/ICCV.2015.352"},{"key":"1061_CR40","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., & Ghanem, B. (2016). A benchmark and simulator for uav tracking. In Proceedings of the European conference on computer vision.","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"1061_CR41","doi-asserted-by":"crossref","unstructured":"Nam, H., & Han, B. (2016). Learning multi-domain convolutional neural networks for visual tracking. In Computer vision and pattern recognition (pp. 4293\u20134302).","DOI":"10.1109\/CVPR.2016.465"},{"key":"1061_CR42","doi-asserted-by":"crossref","unstructured":"Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., et al. (2016). Hedged deep tracking. In CVPR (pp. 4303\u20134311).","DOI":"10.1109\/CVPR.2016.466"},{"issue":"7","key":"1061_CR43","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1109\/TPAMI.2013.230","volume":"36","author":"A Smeulders","year":"2014","unstructured":"Smeulders, A., Chu, D., Cucchiara, R., Calderara, S., Dehghan, A., & Shah, M. (2014). Visual tracking: An experimental survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1442\u20131468.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"7","key":"1061_CR44","doi-asserted-by":"crossref","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. (2009). Learning color names for real-world applications. IEEE Transactions on Image Processing, 18(7), 1512\u20131523.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1061_CR45","unstructured":"Vojir, T., & Matas, J. (2017). Pixel-wise object segmentations for the VOT 2016 dataset. Research Report CTU-CMP-2017-01, Center for Machine Perception, K13133 FEE Czech Technical University, Prague, Czech Republic."},{"key":"1061_CR46","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., & Lu, H. (2015). Visual tracking with fully convolutional networks. In International conference on computer vision (pp. 3119\u20133127).","DOI":"10.1109\/ICCV.2015.357"},{"key":"1061_CR47","unstructured":"Wang, N., Li, S., Gupta, A., & Yeung, D. (2015). Transferring rich feature hierarchies for robust visual tracking. CoRR arXiv:1501.04587."},{"key":"1061_CR48","unstructured":"Wang, S., Zhang, S., Liu, W., & Metaxas, D. N. (2016). Visual tracking with reliable memories. In Proceedings of the twenty-fifth international joint conference on artificial intelligence (pp. 3491\u20133497)."},{"key":"1061_CR49","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., & Yang, M. H. (2013). Online object tracking: A benchmark. In Computer vision and pattern recognition (pp. 2411\u20132418).","DOI":"10.1109\/CVPR.2013.312"},{"issue":"9","key":"1061_CR50","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu, Y., Lim, J., & Yang, M. H. (2015). Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1834\u20131848.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1061_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, L., Liu, Q., Zhang, D., & Yang, M. H. (2014). Fast visual tracking via dense spatio-temporal context learning. In Proceedings of the European conference on computer vision (pp. 127\u2013141). Springer.","DOI":"10.1007\/978-3-319-10602-1_9"},{"key":"1061_CR52","doi-asserted-by":"crossref","unstructured":"Zhong, W., Lu, H., & Yang, M. H. (2012). Robust object tracking via sparsity-based collaborative model. In Computer vision and pattern recognition (pp. 1838\u20131845).","DOI":"10.1109\/CVPR.2012.6247882"},{"key":"1061_CR53","doi-asserted-by":"crossref","unstructured":"Zhu, G., Porikli, F., & Li, H. (2016). Beyond local search: Tracking objects everywhere with instance-specific proposals. In The IEEE conference on computer vision and pattern recognition (CVPR) (pp. 943\u2013951).","DOI":"10.1109\/CVPR.2016.108"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11263-017-1061-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-017-1061-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-017-1061-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T12:54:44Z","timestamp":1693400084000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11263-017-1061-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,8]]},"references-count":53,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2018,7]]}},"alternative-id":["1061"],"URL":"https:\/\/doi.org\/10.1007\/s11263-017-1061-3","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,8]]}}}