{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T14:48:34Z","timestamp":1648997314476},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2013,1,15]],"date-time":"2013-01-15T00:00:00Z","timestamp":1358208000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"published-print":{"date-parts":[[2013,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Online tracking has shown to be successful in tracking of previously unknown objects. However, there are two important factors which lead to drift problem of online tracking, the one is how to select the exact labeled samples even when the target locations are inaccurate, and the other is how to handle the confusors which have similar features with the target. In this article, we propose a robust online tracking algorithm with adaptive samples selection based on saliency detection to overcome the drift problem. To deal with the problem of degrading the classifiers using mis-aligned samples, we introduce the saliency detection method to our tracking problem. Saliency maps and the strong classifiers are combined to extract the most correct positive samples. Our approach employs a simple yet saliency detection algorithm based on image spectral residual analysis. Furthermore, instead of using the random patches as the negative samples, we propose a reasonable selection criterion, in which both the saliency confidence and similarity are considered with the benefits that confusors in the surrounding background are incorporated into the classifiers update process before the drift occurs. The tracking task is formulated as a binary classification via online boosting framework. Experiment results in several challenging video sequences demonstrate the accuracy and stability of our tracker.<\/jats:p>","DOI":"10.1186\/1687-6180-2013-6","type":"journal-article","created":{"date-parts":[[2013,1,15]],"date-time":"2013-01-15T07:14:11Z","timestamp":1358234051000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Robust online tracking via adaptive samples selection with saliency detection"],"prefix":"10.1186","volume":"2013","author":[{"given":"Jia","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiu Ping","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2013,1,15]]},"reference":[{"key":"395_CR1","first-page":"47","volume-title":"Proceedings of British Machine Vision Conference (BMVC)","author":"H Grabner","year":"2006","unstructured":"Grabner H, Grabner M, Bischof H: Real-time tracking via online boosting. In Proceedings of British Machine Vision Conference (BMVC). Edinburgh; 2006:47-56."},{"key":"395_CR2","first-page":"983","volume-title":"IEEE Proceedings of the CVPR","author":"B Babenko","year":"2009","unstructured":"Babenko B, Yang M-H, Belongie S: Visual tracking with online multiple instance learning. In IEEE Proceedings of the CVPR. Miami; 2009:983-990."},{"key":"395_CR3","first-page":"81","volume-title":"IEEE International Conference on Computer Vision","author":"M Godec","year":"2011","unstructured":"Godec M: Hough-based tracking of non-rigid objects. In IEEE International Conference on Computer Vision. Barcelona; 2011:81-88."},{"key":"395_CR4","volume-title":"European Conference on Computer Vision (ECCV)","author":"K Zhang","year":"2012","unstructured":"Zhang K, Zhang L, Yang MH: Real-time compressive tracking. In European Conference on Computer Vision (ECCV). Firenze; 2012."},{"issue":"2","key":"395_CR5","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1109\/TPAMI.2007.35","volume":"29","author":"S Avidan","year":"2007","unstructured":"Avidan S: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(2):261-271.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"395_CR6","first-page":"1393","volume-title":"IEEE International Conferenceon Computer Vision Workshops","author":"A Saffari","year":"2009","unstructured":"Saffari A, Leistner C, Santner J, Godec M, Bischof H: On-line random forests. In IEEE International Conferenceon Computer Vision Workshops. Xi\u2019an; 2009:1393-1400."},{"key":"395_CR7","first-page":"234","volume-title":"European Conference on Computer Vision (ECCV)","author":"H Grabner","year":"2008","unstructured":"Grabner H, Leistner C, Bischof H: Semi-supervised on-line boosting for robust tracking. In European Conference on Computer Vision (ECCV). Marseille; 2008:234-247."},{"key":"395_CR8","first-page":"49","volume-title":"IEEE Proceedings of the CVPR","author":"Z Kalal","year":"2010","unstructured":"Kalal Z, Matas J, Mikolajczyk K: P-n learning: boostrapping binary classifier by structural constraints. In IEEE Proceedings of the CVPR. San Francisco; 2010:49-56."},{"key":"395_CR9","first-page":"678","volume-title":"European Conference on Computer Vision (ECCV)","author":"Q Yu","year":"2008","unstructured":"Yu Q, Dinh TB, Medioni G: Online tracking and reacquisition using co-trained generative and discriminative trackers. In European Conference on Computer Vision (ECCV). Marseille; 2008:678-691."},{"key":"395_CR10","first-page":"1459","volume-title":"IEEE International Conference on Computer Vision","author":"R Liu","year":"2009","unstructured":"Liu R, Cheng J, Lu H: A robust boosting tracker with minimum error bound in a co-training framework. In IEEE International Conference on Computer Vision. Xi\u2019an; 2009:1459-1466."},{"key":"395_CR11","first-page":"539","volume-title":"Automatic Face and Gesture Recognition","author":"H Lu","year":"2011","unstructured":"Lu H, Zhou Q, Wang D, Ruan X: A co-training framework for visual tracking with multiple instance. Automatic Face and Gesture Recognition 2011, 539-544."},{"issue":"11","key":"395_CR12","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/34.730558","volume":"20","author":"L Itti","year":"1998","unstructured":"Itti L, Koch C, Niebur E: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 20(11):1254-1259. 10.1109\/34.730558","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"395_CR13","first-page":"1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"X Hou","year":"2007","unstructured":"Hou X, Zhang L: Saliency detection: a spectral residual approach. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Minnesota; 2007:1-8."},{"key":"395_CR14","volume-title":"Global contrast based salient region detection, inIEEE Proceedings of the CVPR","author":"MM Cheng","year":"2011","unstructured":"Cheng MM, Zhang GX, Mitra NJ, Xiaolei H, Hu SM: Global contrast based salient region detection, inIEEE Proceedings of the CVPR. Springs, Colorado; 2011. pp. 409\u2013416"},{"key":"395_CR15","first-page":"1597","volume-title":"IEEE Proceedings of the CVPR","author":"R Achanata","year":"2009","unstructured":"Achanata R, Hemami S, Estrada F, Susstrunk S: Frequency-tuned salient region detection. In IEEE Proceedings of the CVPR. Miami; 2009:1597-1604."},{"key":"395_CR16","doi-asserted-by":"publisher","first-page":"2462","DOI":"10.1364\/JOSAA.23.002462","volume":"23","author":"B Ko","year":"2006","unstructured":"Ko B, Nam J: Object-of-interest image segmentation based on human attention and semantic region clustering. J. Opt. Soc. Am. 2006, 23: 2462-2470. 10.1364\/JOSAA.23.002462","journal-title":"J. Opt. Soc. Am"},{"key":"395_CR17","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/3-540-36181-2_47","volume":"2525","author":"D Walther","year":"2002","unstructured":"Walther D, Itti L, Riesenhuber M, Poggio T, Koch C: Attentional selection for object recognition\u2014a gentle way. Lect. Notes Comput. Sc. 2002, 2525: 472-479. 10.1007\/3-540-36181-2_47","journal-title":"Lect. Notes Comput. Sc"},{"key":"395_CR18","first-page":"1","volume":"29","author":"H Wu","year":"2010","unstructured":"Wu H, Wang YS, Feng KC, Wong TT, Lee TY, Heng PA: Resizing by symmetry-summarization. ACM Trans. Graph. 2010, 29: 1-9.","journal-title":"ACM Trans. Graph"},{"key":"395_CR19","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997, 55: 119-139. 10.1006\/jcss.1997.1504","journal-title":"J. Comput. Syst. Sci"},{"key":"395_CR20","first-page":"105","volume-title":"Proceedings of Artificial Intelligence and Statistics","author":"N Oza","year":"2001","unstructured":"Oza N, Russell S: Online bagging and boosting. Proceedings of Artificial Intelligence and Statistics 2001, 105-112."},{"key":"395_CR21","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","volume":"77","author":"DA Ross","year":"2008","unstructured":"Ross DA, Lim J, Lin RS, Yang MH: Incremental learning for robust visual tracking. Int. J. Comput. Vis 2008, 77: 125-141. 10.1007\/s11263-007-0075-7","journal-title":"Int. J. Comput. Vis"},{"key":"395_CR22","first-page":"1208","volume-title":"IEEE Proceedings of the CVPR","author":"J Kwon","year":"2009","unstructured":"Kwon J, Lee K: Tracking of a non-rigid object via patch based dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In IEEE Proceedings of the CVPR. Miami; 2009:1208-1215."}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/1687-6180-2013-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/1687-6180-2013-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1687-6180-2013-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T22:44:37Z","timestamp":1630536277000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/1687-6180-2013-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,1,15]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2013,12]]}},"alternative-id":["395"],"URL":"https:\/\/doi.org\/10.1186\/1687-6180-2013-6","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,1,15]]},"assertion":[{"value":"27 September 2012","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2012","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2013","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"6"}}