{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:15:56Z","timestamp":1649204156305},"reference-count":14,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2019,3,1]]},"DOI":"10.1587\/transinf.2018edl8214","type":"journal-article","created":{"date-parts":[[2019,2,28]],"date-time":"2019-02-28T17:38:39Z","timestamp":1551375519000},"page":"684-687","source":"Crossref","is-referenced-by-count":0,"title":["Faster-ADNet for Visual Tracking"],"prefix":"10.1587","volume":"E102.D","author":[{"given":"Tiansa","family":"ZHANG","sequence":"first","affiliation":[{"name":"School of Automation, Beijing Institute of Technology"},{"name":"National Lab. Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"HUO","sequence":"additional","affiliation":[{"name":"National Lab. Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"ZHOU","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"WANG","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] S. Hong, T. You, S. Kwak, and B. Han, \u201cOnline tracking by learning discriminative saliency map with convolutional neural network,\u201d International Conference on Machine Learning, pp.597-606, 2015."},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] M. Danelljan, G. H\u00e4ger, F.S. Khan, and M. Felsberg, \u201cAdaptive decontamination of the training set: A unified formulation for discriminative visual tracking,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.1430-1438, 2016. 10.1109\/cvpr.2016.159","DOI":"10.1109\/CVPR.2016.159"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] H. Fan and H. Ling, \u201cParallel tracking and verifying: A framework for real-time and high accuracy visual tracking,\u201d Proc. IEEE Int. Conf. Computer Vision, Venice, Italy, pp.5487-5495, 2017. 10.1109\/iccv.2017.585","DOI":"10.1109\/ICCV.2017.585"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Y. Qi, S. Zhang, L. Qin, H. Yao, Q. Huang, J. Lim, and M.-H. Yang, \u201cHedged deep tracking,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.4303-4311, 2016. 10.1109\/cvpr.2016.466","DOI":"10.1109\/CVPR.2016.466"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] H. Nam and B. Han, \u201cLearning multi-domain convolutional neural networks for visual tracking,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.4293-4302, 2016. 10.1109\/cvpr.2016.465","DOI":"10.1109\/CVPR.2016.465"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] S. Yun, J. Choi, Y. Yoo, K. Yun, and J.Y. Choi, \u201cAction-decision networks for visual tracking with deep reinforcement learning,\u201d 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.1349-1358, 2017. 10.1109\/cvpr.2017.148","DOI":"10.1109\/CVPR.2017.148"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] R. Hadsell, S. Chopra, and Y. LeCun, \u201cDimensionality reduction by learning an invariant mapping,\u201d 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1735-1742, 2006. 10.1109\/cvpr.2006.100","DOI":"10.1109\/CVPR.2006.100"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] M. Kristan, R. Pflugfelder, A. Leonardis, J. Matas, F. Porikli, L. Cehovin, G. Nebehay, G. Fernandez, T. Vojir, et al., \u201cThe visual object tracking VOT2013 challenge results,\u201d 2013 IEEE International Conference on Computer Vision Workshops, pp.98-111, 2013. 10.1109\/iccvw.2013.20","DOI":"10.1109\/ICCVW.2013.20"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] M. Kristan, R. Pflugfelder, A. Leonardis, J. Matas, L. \u010cehovin, G. Nebehay, T. Voj\u00ed\u0159, G. Fern\u00e1ndez, A. Luke\u017ei\u010d, et al., \u201cThe visual object tracking VOT2014 challenge results,\u201d International Conference on Computer Vision, pp.191-217, 2014. 10.1007\/978-3-319-16181-5_14","DOI":"10.1007\/978-3-319-16181-5_14"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] M. Kristan, J. Matas, A. Leonardis, M. Felsberg, L. Cehovin, G. Fernandez, T. Vojir, G. Hager, G. Nebehay, R. Pflugfelder, A. Gupta, A. Bibi, A. Lukezic, A. Garcia-Martin, A. Saffari, A. Petrosino, and A.S. Montero, \u201cThe visual object tracking VOT2015 challenge results,\u201d International Conference on Computer Vision Workshop, pp.564-586, 2015. 10.1109\/iccvw.2015.79","DOI":"10.1109\/ICCVW.2015.79"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] 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":"12","doi-asserted-by":"crossref","unstructured":"[12] Y. Wu, J. Lim, and M.-H. Yang, \u201cOnline object tracking: A benchmark,\u201d 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.2411-2418, 2013. 10.1109\/cvpr.2013.312","DOI":"10.1109\/CVPR.2013.312"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] M. Danelljan, G. H\u00e4ger, F.S. Khan, and M. Felsberg, \u201cConvolutional features for correlation filter based visual tracking,\u201d International Conference on Computer Vision, pp.621-629, 2016. 10.1109\/iccvw.2015.84","DOI":"10.1109\/ICCVW.2015.84"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] A.W.M. Smeulders, D.M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, \u201cVisual tracking: An experimental survey,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.36, no.7, pp.1442-1468, 2014. 10.1109\/tpami.2013.230","DOI":"10.1109\/TPAMI.2013.230"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/3\/E102.D_2018EDL8214\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,3,2]],"date-time":"2019-03-02T00:21:32Z","timestamp":1551486092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/3\/E102.D_2018EDL8214\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,1]]},"references-count":14,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2018edl8214","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,1]]}}}