{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:26:54Z","timestamp":1772832414280,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2016,7,4]],"date-time":"2016-07-04T00:00:00Z","timestamp":1467590400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of P. R. China","doi-asserted-by":"crossref","award":["61173163"],"award-info":[{"award-number":["61173163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of P. R. China","doi-asserted-by":"crossref","award":["61370200"],"award-info":[{"award-number":["61370200"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multidim Syst Sign Process"],"published-print":{"date-parts":[[2017,7]]},"DOI":"10.1007\/s11045-016-0431-2","type":"journal-article","created":{"date-parts":[[2016,7,4]],"date-time":"2016-07-04T12:06:36Z","timestamp":1467633996000},"page":"1091-1108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel target tracking method based on OSELM"],"prefix":"10.1007","volume":"28","author":[{"given":"Jing","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Lin","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Laihang","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,7,4]]},"reference":[{"key":"431_CR1","doi-asserted-by":"crossref","unstructured":"Adam, A., Rivlin, E., & Shimshoni, I. (2006). Robust fragments-based tracking using the integral histogram. In 2006 IEEE computer society conference on computer vision and pattern recognition (Vol. 1, pp. 798\u2013805). IEEE, 2006.","DOI":"10.1109\/CVPR.2006.256"},{"key":"431_CR2","doi-asserted-by":"crossref","unstructured":"Ahmed, S. M., & Khalifa, O. O. (2014). Vision-based detection and tracking of moving target in video surveillance. In 2014 international conference on computer and communication engineering (ICCCE) (pp. 16\u201319). IEEE, 2014.","DOI":"10.1109\/ICCCE.2014.18"},{"issue":"10","key":"431_CR3","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TCYB.2014.2298235","volume":"44","author":"Z Bai","year":"2014","unstructured":"Bai, Z., Huang, G. B., Wang, D., et al. (2014). Sparse extreme learning machine for classification. IEEE Transactions on Cybernetics, 44(10), 1858\u20131870.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"431_CR4","doi-asserted-by":"crossref","unstructured":"Birchfield, S. T., & Rangarajan, S. (2005). Spatiograms versus histogramsfor region-based tracking. In IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005 (Vol. 2, pp. 1158\u20131163). IEEE, 2005.","DOI":"10.1109\/CVPR.2005.330"},{"issue":"1","key":"431_CR5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1137\/060657704","volume":"51","author":"AM Bruckstein","year":"2009","unstructured":"Bruckstein, A. M., Donoho, D. L., & Elad, M. (2009). From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review, 51(1), 34\u201381.","journal-title":"SIAM Review"},{"key":"431_CR6","unstructured":"Candes, E. J., & Tao, T. (2004). Near-optimal signal recovery from random projections and universal encoding strategies. Submitted to IEEE Trans. Inform. Theory, November."},{"key":"431_CR7","doi-asserted-by":"crossref","unstructured":"Donoho, D. L., & Huo, X. (2000). Beamlet pyramids: A new form of multiresolution analysis suited for extracting lines, curves, and objects from very noisy image data. In International symposium on optical science and technology (pp. 434\u2013444). International Society for Optics and Photonics, 2000.","DOI":"10.1117\/12.408630"},{"issue":"4","key":"431_CR8","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"DL Donoho","year":"2006","unstructured":"Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289\u20131306.","journal-title":"IEEE Transactions on Information Theory"},{"key":"431_CR9","doi-asserted-by":"crossref","unstructured":"Grabner, H., Grabner, M., & Bischof, H. (2006). Real-time tracking via on-line boosting. In BMVC, 2006, Vol. 1, no. 5, p. 6.","DOI":"10.5244\/C.20.6"},{"issue":"2","key":"431_CR10","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MSP.2005.1406476","volume":"22","author":"A Hampapur","year":"2005","unstructured":"Hampapur, A., Brown, L., Connell, J., et al. (2005). Smart video surveillance: Exploring the concept of multiscale spatiotemporal tracking. Signal Processing Magazine, IEEE, 22(2), 38\u201351.","journal-title":"Signal Processing Magazine, IEEE"},{"key":"431_CR11","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.isatra.2015.06.007","volume":"58","author":"YL He","year":"2015","unstructured":"He, Y. L., Geng, Z. Q., Xu, Y., et al. (2015). A robust hybrid model integrating enhanced inputs based extreme learning machine with PLSR (PLSR-EIELM) and its application to intelligent measurement. ISA Transactions, 58, 533\u2013542.","journal-title":"ISA Transactions,"},{"key":"431_CR12","doi-asserted-by":"crossref","unstructured":"He, S., Yang, Q., Lau, R. W. H., et al. (2013). Visual tracking via locality sensitive histograms. In 2013 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2427\u20132434). IEEE, 2013.","DOI":"10.1109\/CVPR.2013.314"},{"key":"431_CR13","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.neucom.2013.08.024","volume":"128","author":"YL He","year":"2014","unstructured":"He, Y. L., Geng, Z. Q., Xu, Y., et al. (2014). A hierarchical structure of extreme learning machine (HELM) for high-dimensional datasets with noise. Neurocomputing, 128, 407\u2013414.","journal-title":"Neurocomputing"},{"issue":"3","key":"431_CR14","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1109\/TIE.2013.2261038","volume":"61","author":"X He","year":"2014","unstructured":"He, X., Wang, Z., Wang, X., et al. (2014). Networked strong tracking filtering with multiple packet dropouts: Algorithms and applications. IEEE Transactions on Industrial Electronics, 61(3), 1454\u20131463.","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"431_CR15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cherd.2015.06.009","volume":"102","author":"YL He","year":"2015","unstructured":"He, Y. L., Geng, Z. Q., & Zhu, Q. X. (2015). Data driven soft sensor development for complex chemical processes using extreme learning machine. Chemical Engineering Research and Design, 102, 1\u201311.","journal-title":"Chemical Engineering Research and Design"},{"issue":"2","key":"431_CR16","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2012","unstructured":"Huang, G. B., Zhou, H., Ding, X., et al. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2), 513\u2013529.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics"},{"issue":"12","key":"431_CR17","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/TCYB.2014.2307349","volume":"44","author":"G Huang","year":"2014","unstructured":"Huang, G., Song, S., Gupta, J. N. D., et al. (2014). Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 44(12), 2405\u20132417.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"5","key":"431_CR18","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0262-8856(00)00074-3","volume":"19","author":"M Kim","year":"2001","unstructured":"Kim, M., Jeon, J. G., Kwak, J. S., et al. (2001). Moving object segmentation in video sequences by user interaction and automatic object tracking. Image and Vision Computing, 19(5), 245\u2013260.","journal-title":"Image and Vision Computing"},{"key":"431_CR19","doi-asserted-by":"crossref","unstructured":"Kwon, J., & Lee, K. M. (2011). Tracking by sampling trackers. In 2011 IEEE international conference on computer vision (ICCV) (pp. 1195\u20131202). IEEE, 2011.","DOI":"10.1109\/ICCV.2011.6126369"},{"key":"431_CR20","doi-asserted-by":"crossref","unstructured":"Mirza , B., Kok, S., & Dong, F. (2016). Multi-layer online sequential extreme learning machine for image classification. In Proceedings of ELM-2015 (Vol. 1, pp. 39\u201349","DOI":"10.1007\/978-3-319-28397-5_4"},{"key":"431_CR21","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.neucom.2014.03.075","volume":"149","author":"B Mirza","year":"2015","unstructured":"Mirza, B., Lin, Z., & Liu, N. (2015). Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing, 149, 316\u2013329.","journal-title":"Neurocomputing"},{"issue":"9","key":"431_CR22","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1002\/cpa.20242","volume":"61","author":"G Peyr","year":"2008","unstructured":"Peyr, G., & Mallat, S. (2008). Orthogonal bandelet bases for geometric images approximation. Communications on Pure and Applied Mathematics, 61(9), 1173\u20131212.","journal-title":"Communications on Pure and Applied Mathematics"},{"issue":"4","key":"431_CR23","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TSMCB.2008.2010506","volume":"39","author":"HJ Rong","year":"2009","unstructured":"Rong, H. J., Huang, G. B., Sundararajan, N., et al. (2009). Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(4), 1067\u20131072.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics"},{"issue":"1\u20133","key":"431_CR24","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","volume":"77","author":"DA Ross","year":"2008","unstructured":"Ross, D. A., Lim, J., Lin, R. S., et al. (2008). Incremental learning for robust visual tracking. International Journal of Computer Vision, 77(1\u20133), 125\u2013141.","journal-title":"International Journal of Computer Vision"},{"key":"431_CR25","unstructured":"Scharw\u00e4chter, T., Enzweiler, M., Franke, U., et al. (2014). Stixmantics: A medium-level model for real-time semantic scene understanding. In European conference on computer vision (ECCV) (pp. 533\u2013548). Berlin Heidelberg: Springer."},{"issue":"4","key":"431_CR26","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","volume":"27","author":"J Tang","year":"2016","unstructured":"Tang, J., Deng, C., & Huang, G. B. (2016). Extreme learning machine for multilayer perceptron. IEEE Transactions on Neural Networks and Learning Systems, 27(4), 809\u2013821.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"431_CR27","doi-asserted-by":"crossref","unstructured":"Wang, J., Mao, W., Wang, L., et al. (2015). Online sequential extreme learning machine with new weight-setting strategy for nonstationary time series prediction. In Proceedings of ELM-2014 (Vol. 1, pp. 263\u2013272).","DOI":"10.1007\/978-3-319-14063-6_23"},{"key":"431_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, L., & Yang, M. H. (2012). Real-time compressive tracking. In European conference on computer vision (ECCV) (pp. 864\u2013877). Berlin Heidelberg: Springer.","DOI":"10.1007\/978-3-642-33712-3_62"},{"issue":"12","key":"431_CR29","doi-asserted-by":"crossref","first-page":"4664","DOI":"10.1109\/TIP.2013.2277800","volume":"22","author":"K Zhang","year":"2013","unstructured":"Zhang, K., Zhang, L., & Yang, M. H. (2013). Real-time object tracking via online discriminative feature selection. IEEE Transactions on Image Processing, 22(12), 4664\u20134677.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"2","key":"431_CR30","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11063-013-9295-8","volume":"39","author":"W Zong","year":"2014","unstructured":"Zong, W., & Huang, G. B. (2014). Learning to rank with extreme learning machine. Neural Processing Letters, 39(2), 155\u2013166.","journal-title":"Neural Processing Letters"}],"container-title":["Multidimensional Systems and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11045-016-0431-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-016-0431-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-016-0431-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-016-0431-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T13:27:34Z","timestamp":1568122054000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11045-016-0431-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,4]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2017,7]]}},"alternative-id":["431"],"URL":"https:\/\/doi.org\/10.1007\/s11045-016-0431-2","relation":{},"ISSN":["0923-6082","1573-0824"],"issn-type":[{"value":"0923-6082","type":"print"},{"value":"1573-0824","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,4]]}}}