{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:25:01Z","timestamp":1747196701793,"version":"3.40.5"},"reference-count":19,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2014]]},"DOI":"10.1587\/transinf.2014edl8129","type":"journal-article","created":{"date-parts":[[2014,11,30]],"date-time":"2014-11-30T23:30:16Z","timestamp":1417390216000},"page":"3248-3251","source":"Crossref","is-referenced-by-count":0,"title":["Motion Detection Algorithm for Unmanned Aerial Vehicle Nighttime Surveillance"],"prefix":"10.1587","volume":"E97.D","author":[{"given":"Huaxin","family":"XIAO","sequence":"first","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"LIU","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"WANG","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maojun","family":"ZHANG","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] T.H. Cox, C.J. Nagy, M.A. Skoog, I.A. Somers, and R. Warner, Civil UAV capability assessment, NASA, Tech. Rep., draft Version, 2004."},{"key":"2","unstructured":"[2] Unmanned aircraft systems roadmap 2005-2030, Defense Technical Information Center, 2005."},{"key":"3","unstructured":"[3] A. Puri, \u201cA survey of unmanned aerial vehicles (UAV) for traffic surveillance,\u201d Department of computer science and engineering, Tech. Rep., University of South Florida, 2005."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] J. Mairal, M. Elad, and G. Sapiro, \u201cSparse representation for color image restoration,\u201d IEEE Trans. Image Process., vol.17, no.1, pp.53-69, 2008.","DOI":"10.1109\/TIP.2007.911828"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M. Elad and A. Michal, \u201cImage denoising via sparse and redundant representations over learned dictionaries,\u201d IEEE Trans. Image Process., vol.15, no.12, pp.3736-3745, 2006.","DOI":"10.1109\/TIP.2006.881969"},{"key":"6","unstructured":"[6] J. Fadili and J.L. Starck, Sparse representation-based image deconvolution by iterative thresholding, Astronomical Data Analysis, 2006."},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma, \u201cRobust face recognition via sparse representation,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.31, no.2, pp.210-227, 2009.","DOI":"10.1109\/TPAMI.2008.79"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] C. Stauffer and W.E.L. Grimson, \u201cAdaptive background mixture models for real-time tracking,\u201d IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.2, pp.246-252, 1999.","DOI":"10.1109\/CVPR.1999.784637"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] A. Elgammal, D. Harwood, and L. Davis, \u201cNon-parametric model for background subtraction,\u201d European Conference on Computer Vision, ECCV 2000, pp.751-767, Springer Berlin Heidelberg, 2000.","DOI":"10.1007\/3-540-45053-X_48"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] L. Maddalena and A. Petrosino, \u201cA self-organizing approach to background subtraction for visual surveillance applications,\u201d IEEE Trans. Image Process., vol.17, no.7, pp.1168-1177, 2008.","DOI":"10.1109\/TIP.2008.924285"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] O. Barnich and M.V. Droogenbroeck, \u201cViBe: A universal background subtraction algorithm for video sequences,\u201d IEEE Trans. Image Process., vol.20, no.6, pp.1709-1724, 2011.","DOI":"10.1109\/TIP.2010.2101613"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] M. Dikmen and T.S. Huang, \u201cRobust estimation of foreground in surveillance videos by sparse error estimation,\u201d ICPR, pp.1-4, 2008.","DOI":"10.1109\/ICPR.2008.4761910"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] V. Cevher, A. Sankaranarayanan, M.F. Duarte, D. Reddy, R.G. Baraniuk, and R. Chellappa, \u201cCompressive sensing for background subtraction,\u201d European Conference on Computer Vision, ECCV, pp.155-168, Springer Berlin Heidelberg, 2008.","DOI":"10.1007\/978-3-540-88688-4_12"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] V. Cevher, V.M.F. Duarte, C. Hegde, and R. Baraniuk, \u201cSparse signal recovery using Markov random fields,\u201d Advances in Neural Information Processing Systems, pp.257-264, 2008.","DOI":"10.21236\/ADA520187"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] J. Huang, X. Huang, and D. Metaxas, \u201cLearning with dynamic group sparsity,\u201d 2009 IEEE 12th International Conference on Computer Vision, pp.64-71, 2009.","DOI":"10.1109\/ICCV.2009.5459202"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] Z. Cong, X. Wang, and W.K. Cham, \u201cBackground subtraction via robust dictionary learning,\u201d EURASIP J. Image and Video Processing, vol.2011, pp.1-12, 2011.","DOI":"10.1155\/2011\/972961"},{"key":"17","unstructured":"[17] J. Mairal, F. Bach, J. Ponce, and G. Sapiro, \u201cOnline learning for matrix factorization and sparse coding,\u201d J. Mach. Learn. Research, vol.11, pp.19-60, 2010."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, \u201cLeast angle regression,\u201d The Annals of statistics, vol.32, no.2, pp.407-499, 2004.","DOI":"10.1214\/009053604000000067"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] X.W. Zhou, C. Yang, and W.C. Yu, \u201cMoving object detection by detecting contiguous outliers in the low-rank representation,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.35, no.3, pp.597-610, 2013.","DOI":"10.1109\/TPAMI.2012.132"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E97.D\/12\/E97.D_2014EDL8129\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T21:51:42Z","timestamp":1747173102000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E97.D\/12\/E97.D_2014EDL8129\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":19,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2014]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2014edl8129","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2014]]}}}