{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T09:18:42Z","timestamp":1714987122138},"reference-count":29,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2022,6,1]]},"DOI":"10.1587\/transinf.2021edp7140","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T22:13:55Z","timestamp":1654035235000},"page":"1225-1233","source":"Crossref","is-referenced-by-count":2,"title":["Reinforced Tracker Based on Hierarchical Convolutional Features"],"prefix":"10.1587","volume":"E105.D","author":[{"given":"Xin","family":"ZENG","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering and Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering"}]},{"given":"Lin","family":"ZHANG","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering and Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering"}]},{"given":"Zhongqiang","family":"LUO","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering and Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering"}]},{"given":"Xingzhong","family":"XIONG","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering and Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering"}]},{"given":"Chengjie","family":"LI","sequence":"additional","affiliation":[{"name":"Southwest Minzu University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] D. Mohanapriya and K. Mahesh, \u201cAn efficient framework for object tracking in video surveillance,\u201d The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems, pp.65-74, 2020. 10.1016\/B978-0-12-816385-6.00005-2","DOI":"10.1016\/B978-0-12-816385-6.00005-2"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] H.J. Asl, \u201cRobust vision-based tracking control of VTOL unmanned aerial vehicles,\u201d Autom., vol.107, pp.425-432, Sept. 2019. 10.1016\/j.automatica.2019.06.004","DOI":"10.1016\/j.automatica.2019.06.004"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] M.Y. Abbass, K.C. Kwon, N. Kim, S.A. Abdelwahab, F.E.A. El-Samie, and A.A.M. Khalaf, \u201cA survey on online learning for visual tracking,\u201d The Visual Computer, pp.1-22, May 2020. 10.1007\/s00371-020-01848-y","DOI":"10.1007\/s00371-020-01848-y"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] M. Fern\u00e1ndez-Sanjurjo, B. Bosquet, M. Mucientes, and V.M. Brea, \u201cReal-time visual detection and tracking system for traffic monitoring,\u201d Eng. Appl. Artif. Intell., vol.85, pp.410-420, Oct. 2019. 10.1016\/j.engappai.2019.07.005","DOI":"10.1016\/j.engappai.2019.07.005"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] M.M. Islam, M.R. Islam, and M.S. Islam, \u201cAn efficient human computer interaction through hand gesture using deep convolutional neural network,\u201d SN Comput. Sci., vol.1, no.4, pp.1-9, June 2020. 10.1007\/s42979-020-00223-x","DOI":"10.1007\/s42979-020-00223-x"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] D.S. Bolme, J.R. Beveridge, B.A. Draper, and Y.M. Lui, \u201cVisual object tracking using adaptive correlation filters,\u201d 23rd IEEE Computer Society Conf. Comput. Vis. Pattern Recognit., pp.2544-2550, San Francisco, CA, USA, June 2010. 10.1109\/CVPR.2010.5539960","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J.F. Henriques, R. Caseiro, P. Martins, and J. Batista, \u201cExploiting the circulant structure of tracking-by-detection with kernels,\u201d Computer Vision-ECCV 2012-12th European Conf. Computer Vision, Florence, Italy, Oct. 7-13, 2012, Proceedings, Part IV, ser. Lect. Notes Comput. Sci., A.W. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C. Schmid, eds., vol.7575, pp.702-715, Springer, 2012. 10.1007\/978-3-642-33765-9_50","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] 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, March 2015. 10.1109\/TPAMI.2014.2345390","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] M. Danelljan, F.S. Khan, M. Felsberg, and J. van de Weijer, \u201cAdaptive color attributes for real-time visual tracking,\u201d 2014 IEEE Conf. Comput. Vis. Pattern Recognit., pp.1090-1097, Columbus, OH, USA, June 2014. 10.1109\/CVPR.2014.143","DOI":"10.1109\/CVPR.2014.143"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] M. Danelljan, G. H\u00e4ger, F.S. Khan, and M. Felsberg, \u201cAccurate scale estimation for robust visual tracking,\u201d British Machine Vision Conference, Nottingham, UK, Sept. 2014, M.F. Valstar, A.P. French, and T.P. Pridmore, eds. BMVA Press, pp.1-11, 2014. 10.5244\/C.28.65","DOI":"10.5244\/C.28.65"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Y. Li and J. Zhu, \u201cA scale adaptive kernel correlation filter tracker with feature integration,\u201d Computer Vision-ECCV 2014 Workshops-Zurich, Switzerland, Sept. 6-7 and 12, 2014, Proceedings, Part II, ser. Lect. Notes Comput. Sci., L. Agapito, M.M. Bronstein, and C. Rother, eds., vol.8926, pp.254-265, Springer, 2014. 10.1007\/978-3-319-16181-5_18","DOI":"10.1007\/978-3-319-16181-5_18"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] C. Ma, X. Yang, C. Zhang, and M.-H Yang, \u201cLong-term correlation tracking,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., pp.5388-5396, Boston, MA, USA, June 2015. 10.1109\/CVPR.2015.7299177","DOI":"10.1109\/CVPR.2015.7299177"},{"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 Conf. Comput. Vis. Pattern Recognit., pp.1401-1409, Las Vegas, NV, USA, June 2016. 10.1109\/CVPR.2016.156","DOI":"10.1109\/CVPR.2016.156"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] M. Wang, Y. Liu, and Z. Huang, \u201cLarge margin object tracking with circulant feature maps,\u201d 2017 IEEE Conf. Comput. Vis. Pattern Recognit., pp.4800-4808, Honolulu, HI, USA, July 2017. 10.1109\/CVPR.2017.510","DOI":"10.1109\/CVPR.2017.510"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] H.K. Galoogahi, A. Fagg, and S. Lucey, \u201cLearning background-aware correlation filters for visual tracking,\u201d IEEE Int. Conf. Comput. Vis., pp.1144-1152, Venice, Italy, Oct. 2017. 10.1109\/ICCV.2017.129","DOI":"10.1109\/ICCV.2017.129"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] Z. Huang, C. Fu, Y. Li, F. Lin, and P. Lu, \u201cLearning aberrance repressed correlation filters for real-time UAV tracking,\u201d 2019 IEEE\/CVF Int. Conf. Comput. Vis., pp.2891-2900, Seoul, Korea (South), Oct. 27-Nov. 2, 2019. 10.1109\/ICCV.2019.00298","DOI":"10.1109\/ICCV.2019.00298"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] C. Ma, J. Huang, X. Yang, and M. Yang, \u201cHierarchical convolutional features for visual tracking,\u201d 2015 IEEE Int. Conf. Comput. Vis., pp.3074-3082, Santiago, Chile, Dec. 2015. 10.1109\/ICCV.2015.352","DOI":"10.1109\/ICCV.2015.352"},{"key":"18","unstructured":"[18] D. Zhang, Z. Zheng, R. Jia, and M. Li, \u201cVisual tracking via hierarchical deep reinforcement learning,\u201d 35th AAAI Conf. Artificial Intelligence, AAAI 2021, 33rd Conf. Innovative Applications of Artificial Intelligence, IAAI 2021, 11th Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, pp.3315-3323, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16443, Feb. 2021."},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] X. Zhou, V. Koltun, and P. Kr\u00e4henb\u00fchl, \u201cTracking objects as points,\u201d Computer Vision-ECCV 2020-16th European Conference, Glasgow, UK, Aug. 23-28, 2020, Proceedings, Part IV, ser. Lect. Notes Comput. Sci., A. Vedaldi, H. Bischof, T. Brox, and J. Frahm, eds., vol.12349, pp.474-490, Springer, 2020. [Online]. Available: https:\/\/doi.org\/10.1007\/978-3-030-58548-8_28 10.1007\/978-3-030-58548-8_28","DOI":"10.1007\/978-3-030-58548-8_28"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] J. Dequaire, P. Ondr\u00fa\u0161ka, D. Rao, D. Wang, and I. Posner, \u201cDeep tracking in the wild: End-to-end tracking using recurrent neural networks,\u201d Int. J. Robotics Res., vol.37, no.4-5, pp.492-512, 2018. [Online]. Available: https:\/\/doi.org\/10.1177\/0278364917710543 10.1177\/0278364917710543","DOI":"10.1177\/0278364917710543"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] X. Li, W. Luo, Y. Zhu, H. Li, and W. Mingwen, \u201cFast deep tracking via semi-online domain adaptation,\u201d J. Physics: Conference Series, vol.1004, pp.1-6, April 2018. 10.1088\/1742-6596\/1004\/1\/012013","DOI":"10.1088\/1742-6596\/1004\/1\/012013"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu, \u201cHigh performance visual tracking with siamese region proposal network,\u201d 2018 IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., pp.8971-8980, Salt Lake City, UT, USA, June 2018. [Online]. Available: http:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Li_High_Performance_Visual_CVPR_2018_paper.html 10.1109\/CVPR.2018.00935","DOI":"10.1109\/CVPR.2018.00935"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] Q. Wang, L. Zhang, L. Bertinetto, W. Hu, and P.H.S. Torr, \u201cFast online object tracking and segmentation: A unifying approach,\u201d IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., pp.1328-1338, Long Beach, CA, USA, June 2019. [Online]. Available: http:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Wang_Fast_Online_Object_Tracking_and_Segmentation_A_Unifying_Approach_CVPR_2019_paper.html 10.1109\/CVPR.2019.00142","DOI":"10.1109\/CVPR.2019.00142"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] M. Danelljan, G. Bhat, F.S. Khan, and M. Felsberg, \u201cECO: efficient convolution operators for tracking,\u201d 2017 IEEE Conf. Comput. Vis. Pattern Recognit., pp.6931-6939, Honolulu, HI, USA, July 2017. 10.1109\/CVPR.2017.733","DOI":"10.1109\/CVPR.2017.733"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] Y. Wu, J. Lim, and M.-H. Yang, \u201cOnline object tracking: A benchmark,\u201d 2013 IEEE Conf. Comput. Vis. Pattern Recognit., pp.2411-2418, Portland, OR, USA, June 2013. 10.1109\/CVPR.2013.312","DOI":"10.1109\/CVPR.2013.312"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImagenet classification with deep convolutional neural networks,\u201d Commun. ACM, vol.60, no.6, pp.84-90, June 2017. 10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"27","unstructured":"[27] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d 3rd Int. Conf. Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Y. Bengio and Y. LeCun, eds., pp.1-14, 2015. 10.48550\/arXiv.1409.1556"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] Y. Wang, X. Wei, L. Ding, X. Tang, and H. Zhang, \u201cA robust visual tracking method via local feature extraction and saliency detection,\u201d Vis. Comput., vol.36, no.4, pp.683-700, 2020. 10.1007\/s00371-019-01646-1","DOI":"10.1007\/s00371-019-01646-1"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] L. Gong, Z. Mo, S. Zhao, and Y. Song, \u201cAn improved kernelized correlation filter tracking algorithm based on multi-channel memory model,\u201d Signal Process. Image Commun., vol.78, pp.200-205, Oct. 2019. 10.1016\/j.image.2019.05.019","DOI":"10.1016\/j.image.2019.05.019"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/6\/E105.D_2021EDP7140\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,4]],"date-time":"2022-06-04T04:34:52Z","timestamp":1654317292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/6\/E105.D_2021EDP7140\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2021edp7140","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,1]]},"article-number":"2021EDP7140"}}