{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:36:52Z","timestamp":1762868212400,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876153"],"award-info":[{"award-number":["61876153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2452019180"],"award-info":[{"award-number":["2452019180"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11760-020-01731-2","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T22:02:27Z","timestamp":1594159347000},"page":"121-128","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improved MDNet tracking with fast feature extraction and efficient multiple domain training"],"prefix":"10.1007","volume":"15","author":[{"given":"Haoyue","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jifeng","family":"Ning","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangchen","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Ni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"issue":"8","key":"1731_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., 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":"1731_CR2","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: Fully-convolutional siamese networks for object tracking. In: European Conference on Computer Vision, pp. 850\u2013865 (2016)","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"1731_CR3","doi-asserted-by":"crossref","unstructured":"Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: Proceedings of the British Machine Vision Conference (2014)","DOI":"10.5244\/C.28.6"},{"key":"1731_CR4","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M., et al.: Eco: Efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 713\u2013730 (2017)","DOI":"10.1109\/CVPR.2017.733"},{"key":"1731_CR5","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference (2014)","DOI":"10.5244\/C.28.65"},{"key":"1731_CR6","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Shahbaz\u00a0Khan, F., Felsberg, M.: Convolutional features for correlation filter based visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 58\u201366 (2015)","DOI":"10.1109\/ICCVW.2015.84"},{"key":"1731_CR7","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Shahbaz\u00a0Khan, 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":"1731_CR8","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Robinson, A., Khan, F.S., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: European Conference on Computer Vision, pp. 472\u2013488 (2016)","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"1731_CR9","doi-asserted-by":"crossref","unstructured":"Fan, H., Ling, H.: Sanet: Structure-aware network for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2217\u20132224 (2017)","DOI":"10.1109\/CVPRW.2017.275"},{"key":"1731_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1731_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"1731_CR12","doi-asserted-by":"crossref","unstructured":"Han, B., Sim, J., Adam, H.: Branchout: regularization for online ensemble tracking with convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2217\u20132224 (2017)","DOI":"10.1109\/CVPR.2017.63"},{"issue":"10","key":"1731_CR13","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1109\/TPAMI.2015.2509974","volume":"38","author":"S Hare","year":"2016","unstructured":"Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M., Hicks, S.L., Torr, P.H.S.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096\u20132109 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1731_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"1731_CR15","doi-asserted-by":"crossref","unstructured":"Held, D., Thrun, S., Savarese, S.: Learning to track at 100 fps with deep regression networks. In: European Conference on Computer Vision, pp. 749\u2013765 (2016)","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"1731_CR16","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"J Henriques","year":"2014","unstructured":"Henriques, J., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37, 583\u2013596 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"1731_CR17","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":"1731_CR18","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":"1731_CR19","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.patcog.2017.11.007","volume":"76","author":"P Li","year":"2018","unstructured":"Li, P., Wang, D., Wang, L., Lu, H.: Deep visual tracking: review and experimental comparison. Pattern Recogn. 76, 323\u2013338 (2018)","journal-title":"Pattern Recogn."},{"key":"1731_CR20","doi-asserted-by":"crossref","unstructured":"Lukezic, A., Voj\u00edr, T., Cehovin, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6309\u20136318 (2017)","DOI":"10.1109\/CVPR.2017.515"},{"key":"1731_CR21","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":"1731_CR22","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M., Bibi, A., Giancola, S., Al-Subaihi, S., Ghanem, B.: Trackingnet: a large-scale dataset and benchmark for object tracking in the wild. In: European Conference on Computer Vision, pp. 310\u2013327 (2018)","DOI":"10.1007\/978-3-030-01246-5_19"},{"key":"1731_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":"1731_CR24","doi-asserted-by":"crossref","unstructured":"Park, E., Berg, A.C.: Meta-tracker: fast and robust online adaptation for visual object trackers. In: European Conference on Computer Vision, pp. 569\u2013585 (2018)","DOI":"10.1007\/978-3-030-01219-9_35"},{"key":"1731_CR25","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, 125\u2013141 (2008)","journal-title":"Int. J. Comput. Vis."},{"key":"1731_CR26","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1109\/TPAMI.2013.230","volume":"36","author":"AW Smeulders","year":"2014","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, 1442\u20131468 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1731_CR27","doi-asserted-by":"crossref","unstructured":"Song, Y., Chao, M., Gong, L., Zhang, J., Lau, R.W.H., Yang, M.H.: Crest: convolutional residual learning for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2555\u20132564 (2017)","DOI":"10.1109\/ICCV.2017.279"},{"key":"1731_CR28","doi-asserted-by":"crossref","unstructured":"Tao, R., Gavves, E., Smeulders, A.W.: Siamese instance search for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1420\u20131429 (2016)","DOI":"10.1109\/CVPR.2016.158"},{"key":"1731_CR29","doi-asserted-by":"crossref","unstructured":"Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5000\u20135008 (2017)","DOI":"10.1109\/CVPR.2017.531"},{"key":"1731_CR30","doi-asserted-by":"crossref","unstructured":"Wang, N., Shi, J., Yeung, D., Jia, J.: Understanding and diagnosing visual tracking systems. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3101\u20133109 (2015)","DOI":"10.1109\/ICCV.2015.355"},{"issue":"9","key":"1731_CR31","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."},{"issue":"12","key":"1731_CR32","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1109\/TITS.2017.2686871","volume":"18","author":"Y Yuan","year":"2017","unstructured":"Yuan, Y., Lu, Y., Wang, Q.: Tracking as a whole: multi-target tracking by modeling group behavior with sequential detection. IEEE Trans. Intell. Transp. Syst. 18(12), 3339\u20133349 (2017)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"7","key":"1731_CR33","doi-asserted-by":"publisher","first-page":"1918","DOI":"10.1109\/TITS.2016.2614548","volume":"18","author":"Y Yuan","year":"2016","unstructured":"Yuan, Y., Xiong, Z., Wang, Q.: An incremental framework for video-based traffic sign detection, tracking, and recognition. IEEE Trans. Intell. Transp. Syst. 18(7), 1918\u20131929 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1731_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: European Conference on Computer Vision, pp. 188\u2013203 (2014)","DOI":"10.1007\/978-3-319-10599-4_13"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-020-01731-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-020-01731-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-020-01731-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:56:44Z","timestamp":1625615804000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-020-01731-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,7]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["1731"],"URL":"https:\/\/doi.org\/10.1007\/s11760-020-01731-2","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2020,7,7]]},"assertion":[{"value":"20 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}