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Wang, J. Shi, D.-Y. Yeung, and J. Jia, \u201cUnderstanding and diagnosing visual tracking systems,\u201d In Proc. IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7-13 Dec. 2015, pp.3101-3109, 2015. 10.1109\/iccv.2015.355","DOI":"10.1109\/ICCV.2015.355"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] N. Zeng, H. Li, and Y. Peng, \u201cA new deep belief network-based multi-task learning for diagnosis of Alzheimer's disease,\u201d Neural Computing and Applications, 2021. https:\/\/doi.org\/10.1007\/s00521-021-06149-6 10.1007\/s00521-021-06149-6","DOI":"10.1007\/s00521-021-06149-6"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] Y. Wu, J. Lim, and M.-H. Yang, \u201cOnline object tracking: A benchmark,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.2411-2418, 2013. 10.1109\/cvpr.2013.312","DOI":"10.1109\/CVPR.2013.312"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] 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":"5","doi-asserted-by":"publisher","unstructured":"[5] M. Kristan, J. Matas, A. Leonardis, T. Voj\u00ed\u0159, R. Pflugfelder, G. Fern\u00e1ndez, G. Nebehay, F. Porikli, and L. \u010cehovin \u201cA novel performance evaluation methodology for single-target trackers,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.38, no.11, pp.2137-2155, 2016. 10.1109\/tpami.2016.2516982","DOI":"10.1109\/TPAMI.2016.2516982"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] N. Wax, \u201cSignal-to-Noise Improvement and the Statistics of Track Populations,\u201d Journal of Applied physics, vol.26, no.5, pp.586-595, 1955. 10.1063\/1.1722046","DOI":"10.1063\/1.1722046"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] W. Jiang and L. Zhang, \u201cEdge-siamnet and edge-triplenet: New deep learning models for handwritten numeral recognition,\u201d IEICE Trans. Inf. & Syst., vol.E103-D, no.3, pp.720-723, 2020. 10.1587\/transinf.2019edl8199","DOI":"10.1587\/transinf.2019EDL8199"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] H. Kwon, Y. Kim, H. Yoon, and D. Choi, \u201cCaptcha image generation systems using generative adversarial networks,\u201d IEICE Trans. Inf. & Syst., vol.E101-D, no.2, pp.543-546, 2018. 10.1587\/transinf.2017edl8175","DOI":"10.1587\/transinf.2017EDL8175"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] L. Bertinetto, J. Valmadre, J.F. Henriques, A. Vedaldi, and P.H.S. Torr, \u201cFully-convolutional siamese networks for object tracking,\u201d European Conference on Computer Vision, Springer, Cham, pp.850-865, 2016. 10.1007\/978-3-319-48881-3_56","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu, \u201cHigh performance visual tracking with siamese region proposal network,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.8971-8980, 2018. 10.1109\/cvpr.2018.00935","DOI":"10.1109\/CVPR.2018.00935"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] D.S. Bolme, J.R. Beveridge, B.A. Draper, and Y.M. Lui, \u201cVisual object tracking using adaptive correlation filters,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.2544-2550, 2010. 10.1109\/cvpr.2010.5539960","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[13] C. Ma, J.-B. Huang, X. Yang, M.-H. Yang, \u201cAdaptive correlation filters with long-term and short-term memory for object tracking,\u201d International Journal of Computer Vision, vol.126, no.8, pp.771-796, 2018. 10.1007\/s11263-018-1076-4","DOI":"10.1007\/s11263-018-1076-4"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[14] M. Danelljan, A. Robinson, F.S. Khan, and M. Felsberg, \u201cBeyond correlation filters: Learning continuous convolution operators for visual tracking,\u201d European Conference on Computer Vision, Springer, Cham, pp.472-488, 2016. 10.1007\/978-3-319-46454-1_29","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[15] M. Danelljan, G. Bhat, F.S. Khan, and M. Felsberg, \u201cEco: Efficient convolution operators for tracking,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.6638-6646, 2017. 10.1109\/cvpr.2017.733","DOI":"10.1109\/CVPR.2017.733"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[16] N. Zeng, Z. Wang, W. Liu, H. Zhang, K. Hone, and X. Liu, \u201cA dynamic neighborhood-based switching particle swarm optimization algorithm,\u201d IEEE Trans. Cybern., vol.52, no.9, pp.9290-9301, 2022. doi: 10.1109\/TCYB.2020.3029748. 10.1109\/tcyb.2020.3029748","DOI":"10.1109\/TCYB.2020.3029748"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[17] N. Zeng, D. Song, H. Li, Y. You, Y. Liu, and F. Alsaadic, \u201cA competitive mechanism integrated multi-objective whale optimization algorithm with differential devolution,\u201d Neurocomputing, vol.432, pp.170-182, 2021. 10.1016\/j.neucom.2020.12.065","DOI":"10.1016\/j.neucom.2020.12.065"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[19] J. Van De Weijer, C. Schmid, J. Verbeek, and D. Larlus, \u201cLearning color names for real-world applications,\u201d IEEE Trans. Image Process., vol.18, no.7, pp.1512-1523, 2009. 10.1109\/tip.2009.2019809","DOI":"10.1109\/TIP.2009.2019809"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[20] M. Danelljan, F.S. Khan, M. Felsberg, and J. Van De Weijer, \u201cAdaptive color attributes for real-time visual tracking,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.1090-1097, 2014. 10.1109\/cvpr.2014.143","DOI":"10.1109\/CVPR.2014.143"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[21] M. Wang, Y. Liu, and Z. Huang, \u201cLarge margin object tracking with circulant feature maps,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.4021-4029, 2017. 10.1109\/cvpr.2017.510","DOI":"10.1109\/CVPR.2017.510"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[22] C.L. Zitnick and P. Doll\u00e1r, \u201cEdge boxes: Locating object proposals from edges,\u201d European Conference on Computer Vision, Springer, Cham, pp.391-405, 2014. 10.1007\/978-3-319-10602-1_26","DOI":"10.1007\/978-3-319-10602-1_26"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[23] N. Zeng, Z. Wang, B. Zineddin, Y. Li, M. Du, L. Xiao, X. Liu, and T. Young, \u201cImage-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach,\u201d IEEE Trans. Med. Imag., vol.33, no.5, pp.1129-1136, 2014. 10.1109\/tmi.2014.2305394","DOI":"10.1109\/TMI.2014.2305394"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[24] Z. Hong, Z. Chen, C. Wang, X. Mei, D. Prokhorov, and D. Tao, \u201cMulti-store tracker (muster): A cognitive psychology inspired approach to object tracking,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.749-758, 2015. 10.1109\/cvpr.2015.7298675","DOI":"10.1109\/CVPR.2015.7298675"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[25] C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang, \u201cHierarchical convolutional features for visual tracking,\u201d IEEE International Conference on Computer Vision, pp.3074-3082, 2015. 10.1109\/iccv.2015.352","DOI":"10.1109\/ICCV.2015.352"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[26] Y. Qi, S. Zhang, L. Qin, H. Yao, Q. Huang, J. Lim, and M.-H. Yang, \u201cHedged deep tracking,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.4303-4311, 2016. 10.1109\/cvpr.2016.466","DOI":"10.1109\/CVPR.2016.466"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[27] J. Choi, H.J. Chang, S. Yun, T. Fischer, Y. Demiris, and J.Y. Choi, \u201cAttentional correlation filter network for adaptive visual tracking,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.4807-4816, 2017. 10.1109\/cvpr.2017.513","DOI":"10.1109\/CVPR.2017.513"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[28] J. Valmadre, L. Bertinetto, J. Henriques, A. Vedaldi, and P.H.S. Torr, \u201cEnd-to-end representation learning for correlation filter based tracking,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.2805-2813, 2017. 10.1109\/cvpr.2017.531","DOI":"10.1109\/CVPR.2017.531"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[13] C. Ma, J.-B. Huang, X. Yang, M.-H. Yang, \u201cAdaptive correlation filters with long-term and short-term memory for object tracking,\u201d International Journal of Computer Vision, vol.126, no.8, pp.771-796, 2018. 10.1007\/s11263-018-1076-4","DOI":"10.1007\/s11263-018-1076-4"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[30] 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, 2014. 10.1109\/tpami.2014.2345390","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[31] T. Vojir, J. Noskova, and J. Matas, \u201cRobust scale-adaptive mean-shift for tracking,\u201d Pattern Recognition Letters, vol.49, pp.250-258, 2014. 10.1016\/j.patrec.2014.03.025","DOI":"10.1016\/j.patrec.2014.03.025"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[32] N. Dalal and B. Triggs, \u201cHistograms of oriented gradients for human detection,\u201d IEEE Conference on Computer Vision and Pattern Recognition, vol.1, pp.886-893, 2005. 10.1109\/cvpr.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[33] M. Tang and J. Feng, \u201cMulti-kernel correlation filter for visual tracking,\u201d IEEE International Conference on Computer Vision, pp.3038-3046, 2015. 10.1109\/iccv.2015.348","DOI":"10.1109\/ICCV.2015.348"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[34] H. Possegger, T. Mauthner, and H. Bischof, \u201cIn defense of color-based model-free tracking,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.2113-2120, 2015. 10.1109\/cvpr.2015.7298823","DOI":"10.1109\/CVPR.2015.7298823"},{"key":"33","unstructured":"[35] N. Wang, S. Li, A. Gupta, et al., \u201cTransferring rich feature hierarchies for robust visual tracking,\u201d arXiv preprint arXiv 2015:1501.04587, 2015."},{"key":"34","doi-asserted-by":"crossref","unstructured":"[36] M. Danelljan, G. H\u00e4ger, F.S. Khan, and M. Felsberg, \u201cLearning spatially regularized correlation filters for visual tracking,\u201d IEEE International Conference on Computer Vision, pp.4310-4318, 2015. 10.1109\/iccv.2015.490","DOI":"10.1109\/ICCV.2015.490"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[37] J. Xiao, R. Stolkin, and A. Leonardis, \u201cSingle target tracking using adaptive clustered decision trees and dynamic multi-level appearance models,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.4978-4987, 2015. 10.1109\/cvpr.2015.7299132","DOI":"10.1109\/CVPR.2015.7299132"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[38] X. Wang, M. Valstar, B. Martinez, M.H. Khan, and T. Pridmore, \u201cTric-track: Tracking by regression with incrementally learned cascades,\u201d IEEE International Conference on Computer Vision, pp.4337-4345, 2015. 10.1109\/iccv.2015.493","DOI":"10.1109\/ICCV.2015.493"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[39] H. Nam and B. Han, \u201cLearning multi-domain convolutional neural networks for visual tracking,\u201d IEEE Conference on Computer Vision and Pattern Recognition, pp.4293-4302, 2016. 10.1109\/cvpr.2016.465","DOI":"10.1109\/CVPR.2016.465"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[40] E. Gundogdu and A.A. Alatan, \u201cSpatial windowing for correlation filter based visual tracking,\u201d IEEE International Conference on Image Processing, pp.1684-1688, 2016. 10.1109\/icip.2016.7532645","DOI":"10.1109\/ICIP.2016.7532645"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[41] A. Luke\u017ei\u010d, L.\u010c. Zajc, and M. Kristan, \u201cDeformable parts correlation filters for robust visual tracking,\u201d IEEE Trans. Cybern., vol.48, no.6, pp.1849-1861, 2017. 10.1109\/tcyb.2017.2716101","DOI":"10.1109\/TCYB.2017.2716101"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[42] H. Lee and D. Kim, \u201cSalient region-based online object tracking,\u201d IEEE Winter Conference on Applications of Computer Vision, pp.1170-1177, 2018. 10.1109\/wacv.2018.00133","DOI":"10.1109\/WACV.2018.00133"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/5\/E106.D_2022DLP0039\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T04:55:53Z","timestamp":1715316953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/5\/E106.D_2022DLP0039\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2022dlp0039","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,1]]},"article-number":"2022DLP0039"}}