{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T16:10:44Z","timestamp":1663171844395},"reference-count":20,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2018,5,1]]},"DOI":"10.1587\/transinf.2017edp7245","type":"journal-article","created":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T22:25:32Z","timestamp":1525127132000},"page":"1342-1349","source":"Crossref","is-referenced-by-count":1,"title":["Long-Term Tracking Based on Multi-Feature Adaptive Fusion for Video Target"],"prefix":"10.1587","volume":"E101.D","author":[{"given":"Hainan","family":"ZHANG","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}]},{"given":"Yanjing","family":"SUN","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}]},{"given":"Song","family":"LI","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}]},{"given":"Wenjuan","family":"SHI","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}]},{"given":"Chenglong","family":"FENG","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] Y. Wu, J. Lim, and M.-H. Yang, \u201cObject Tracking Benchmark,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.9, pp.1834-1848, 2015. 10.1109\/tpami.2014.2388226","DOI":"10.1109\/TPAMI.2014.2388226"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] J. Wang, H. Zhu, S. Yu, and C. Fan, \u201cObject tracking using color-feature guided network generalization and tailored feature fusion,\u201d Neurocomputing, vol.238, pp.387-398, 2017. 10.1016\/j.neucom.2017.02.001","DOI":"10.1016\/j.neucom.2017.02.001"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] Q. Miao, C. Zhang, and L. Meng, \u201cFeature-Based On-Line Object Tracking Combining Both Keypoints and Quasi-Keypoints Matching,\u201d IEICE Trans. Inf. & Syst., vol.E99-D, no.4, pp.1264-1267, 2016. 10.1587\/transinf.2015edl8232","DOI":"10.1587\/transinf.2015EDL8232"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] Q. Miao, C. Shi, L. Meng, and G. Cheng, \u201c \u201cOn-Line Rigid Object Tracking via Discriminative Feature Classification,\u201d IEICE Trans. Inf. & Syst., vol.E99-D, no.11, pp.2824-2827, 2016. 10.1587\/transinf.2016edl8098","DOI":"10.1587\/transinf.2016EDL8098"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] S. Avidan, \u201cEnsemble tracking,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.29, no.2, pp.261-271, 2007. 10.1109\/tpami.2007.35","DOI":"10.1109\/TPAMI.2007.35"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] F. Tang, S. Brennan, Q. Zhao, and H. Tao, \u201cCo-Tracking Using Semi-Supervised Support Vector Machines,\u201d 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007. 10.1109\/iccv.2007.4408954","DOI":"10.1109\/ICCV.2007.4408954"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] K. Zhang, L. Zhang, and M.-H. Yang, \u201cReal-time compressive tracking,\u201d Computer Vision ECCV 2012, vol.7574, pp.864-877, 2012. 10.1007\/978-3-642-33712-3_62","DOI":"10.1007\/978-3-642-33712-3_62"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] D.S. Bolme, J.R. Beveridge, B.A. Draper, and Y.M. Lui, \u201cVisual object tracking using adaptive correlation filters,\u201d 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2544-2550, 2010. 10.1109\/cvpr.2010.5539960","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] J.F. Henriques, R. Caseiro, P. Martins, and J. Batista, \u201cExploiting the Circulant Structure of Tracking-by-Detection with Kernels,\u201d Computer Vision-ECCV 2012, Lecture Notes in Computer Science, vol.7575, pp.702-715, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012. 10.1007\/978-3-642-33765-9_50","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] J.F. Henriques, R. Caseiro, P. Martins, and J. Batista, \u201cHigh-Speed Tracking with Kernelized Correlation Filters,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.3, pp.583-596, 2015. 10.1109\/tpami.2014.2345390","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] C. Ma, X. Yang, C. Zhang, and M.-H. Yang, \u201cLong-term correlation tracking,\u201d 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5388-5396, 2015. 10.1109\/cvpr.2015.7299177","DOI":"10.1109\/CVPR.2015.7299177"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] Y. Li and J. Zhu, \u201cA Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration,\u201d Computer Vision-ECCV 2014 Workshops, Lecture Notes in Computer Science, vol.8926, pp.254-265, Springer International Publishing, Cham, 2015. 10.1007\/978-3-319-16181-5_18","DOI":"10.1007\/978-3-319-16181-5_18"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] L. Bertinetto, J. Valmadre, S. Golodetz, O. Miksik, and P.H.S. Torr, \u201cStaple: Complementary Learners for Real-Time Tracking,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1401-1409, 2016. 10.1109\/cvpr.2016.156","DOI":"10.1109\/CVPR.2016.156"},{"key":"14","unstructured":"[14] R. Rifkin, G. Yeo, and T. Poggio, \u201cRegularized least-squares classification,\u201d Nato Science Series Sub Series III Computer and Systems Sciences, vol.190, pp.131-154, 2003."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] P.F. Felzenszwalb, R.B. Girshick, D. McAllester, and D. Ramanan, \u201cObject Detection with Discriminatively Trained Part-Based Models,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.32, no.9, pp.1627-1645, 2010. 10.1109\/tpami.2009.167","DOI":"10.1109\/TPAMI.2009.167"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] T. Ojala, M. Pietikainen, and T. Maenpaa, \u201cMultiresolution gray-scale and rotation invariant texture classification with local binary patterns,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.24, no.7, pp.971-987, 2002. 10.1109\/tpami.2002.1017623","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J. van de Weijer, C. Schmid, and J. Verbeek, \u201cLearning Color Names from Real-World Images,\u201d 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007. 10.1109\/cvpr.2007.383218","DOI":"10.1109\/CVPR.2007.383218"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] S. Hare, S. Golodetz, A. Saffari, V. Vineet, M.-M. Cheng, S.L. Hicks, and P.H.S. Torr, \u201cStruck: Structured Output Tracking with Kernels,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.38, no.10, pp.2096-2109, 2016. 10.1109\/tpami.2015.2509974","DOI":"10.1109\/TPAMI.2015.2509974"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] Z. Kalal, J. Matas, and K. Mikolajczyk, \u201cP-N learning: Bootstrapping binary classifiers by structural constraints,\u201d 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.49-56, 2010. 10.1109\/cvpr.2010.5540231","DOI":"10.1109\/CVPR.2010.5540231"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] T.B. Dinh, N. Vo, and G. Medioni, \u201cContext tracker: Exploring supporters and distracters in unconstrained environments,\u201d CVPR 2011, pp.1177-1184, 2011. 10.1109\/cvpr.2011.5995733","DOI":"10.1109\/CVPR.2011.5995733"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/5\/E101.D_2017EDP7245\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T07:54:45Z","timestamp":1571298885000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/5\/E101.D_2017EDP7245\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,1]]},"references-count":20,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2017edp7245","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,1]]}}}