Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm

An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose wea...

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Vydáno v:	Computational Intelligence and Neuroscience Ročník 2016; číslo 2016; s. 637 - 645-054
Hlavní autoři:	Wang, Li Jia, Zhang, Hua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cairo, Egypt Hindawi Limiteds 01.01.2016 Hindawi Publishing Corporation John Wiley & Sons, Inc
Témata:	Algorithms Ambiguity Artificial Intelligence Attention - physiology Classifiers Distance learning Eye Movements - physiology Feedback Histograms Humans Learning Learning - physiology Machine learning Mathematical research Methods Online Systems Photic Stimulation Probability Strategy Technology application Tracking Visual China
ISSN:	1687-5265, 1687-5273
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Abstract	An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probability M times. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.
AbstractList	An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probability M times. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes. An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probability M times. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.
Audience	Academic
Author	Zhang, Hua Wang, Li Jia
AuthorAffiliation	1 Department of Information Engineering and Automation, Hebei College of Industry and Technology, Shijiazhuang 050091, China 2 Faculty of Electrical & Electronics Engineering, Shijiazhuang Vocational Technology Institute, Shijiazhuang 050081, China
AuthorAffiliation_xml	– name: 1 Department of Information Engineering and Automation, Hebei College of Industry and Technology, Shijiazhuang 050091, China – name: 2 Faculty of Electrical & Electronics Engineering, Shijiazhuang Vocational Technology Institute, Shijiazhuang 050081, China
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26843855$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1016/j.imavis.2011.08.006 10.1109/TPAMI.2003.1233903 10.1007/s11263-009-0275-4 10.1145/1177352.1177355 10.1016/S0004-3702(96)00034-3 10.1016/j.image.2011.06.005 10.1007/978-3-540-88688-4_16 10.1016/j.patcog.2015.06.004 10.1016/j.isatra.2012.02.002 10.1016/j.sigpro.2012.09.011 10.1016/j.patcog.2012.07.013 10.1109/TPAMI.2010.226 10.1109/TPAMI.2008.79 10.1016/j.neucom.2011.11.031 10.1109/tit.2006.885507
ContentType	Journal Article
Copyright	Copyright © 2016 Li Jia Wang and Hua Zhang. COPYRIGHT 2015 John Wiley & Sons, Inc. COPYRIGHT 2016 John Wiley & Sons, Inc. Copyright © 2016 Li Jia Wang and Hua Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2016 L. J. Wang and H. Zhang. 2016
Copyright_xml	– notice: Copyright © 2016 Li Jia Wang and Hua Zhang. – notice: COPYRIGHT 2015 John Wiley & Sons, Inc. – notice: COPYRIGHT 2016 John Wiley & Sons, Inc. – notice: Copyright © 2016 Li Jia Wang and Hua Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright © 2016 L. J. Wang and H. Zhang. 2016
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Online multiple support instance tracking Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG '11) March 2011 Santa Barbara, Calif, USA IEEE 545 552 10.1109/fg.2011.5771456 2-s2.0-79958713693 – volume: 51 start-page: 485 issue: 3 year: 2012 end-page: 497 ident: 4 article-title: Applying mean shift, motion information and Kalman filtering approaches to object tracking – volume: 88 start-page: 303 issue: 2 year: 2010 end-page: 338 ident: 29 article-title: The pascal visual object classes (VOC) challenge – reference: Zhou T. Lu Y. Qiu M. Online visual tracking using multiple instance learning with instance significance estimation http://arxiv.org/abs/1501.04378v1 – reference: Li H. Shen C. Shi Q. Real-time visual tracking using compressive sensing Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11) June 2011 Providence, RI, USA IEEE 1305 1312 10.1109/CVPR.2011.5995483 – volume: 52 start-page: 5406 issue: 12 year: 2006 end-page: 5425 ident: 8 article-title: Near-optimal signal recovery from random projections: universal encoding strategies – volume: 25 start-page: 1296 issue: 10 year: 2003 end-page: 1311 ident: 13 article-title: Robust online appearance models for visual tracking – volume: 48 start-page: 3917 issue: 12 year: 2015 end-page: 3926 ident: 25 article-title: Robust visual tracking via online multiple instance learning – reference: Adam A. Rivlin E. Shimshoni I. Robust fragments-based tracking using the integral histogram 1 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06) June 2006 New York, NY, USA IEEE 798 805 10.1109/cvpr.2006.256 2-s2.0-33845596140 – reference: Dollar P. Babenko B. Belongie S. Perona P. Tu Z. Multiple component learning for object detection Computer Vision—ECCV 2008 2008 5303 Berlin, Germany Springer 211 224 Lecture Notes in Computer Science 10.1007/978-3-540-88688-4_16 – reference: Zivkovic Z. Kröse B. An EM-like algorithm for color-histogram-based object tracking Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04) July 2004 IEEE I798 I803 2-s2.0-5044222503 – volume: 29 start-page: 787 issue: 11 year: 2011 end-page: 796 ident: 12 article-title: Object tracking via appearance modeling and sparse representation – reference: Viola P. Platt J. C. Zhang C. Multiple instance boosting for object detection Proceedings of the Neural Information Processing Systems Conference (NIPS '05) 2005 1417 1426 – reference: Zhang K. Zhang L. Yang M. H. 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Object tracking using color correlogram Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (PETS '05) October 2005 Beijing, China 263 270 10.1109/vspets.2005.1570924 2-s2.0-33846584110 – volume: 33 start-page: 1619 issue: 8 year: 2011 end-page: 1632 ident: 14 article-title: Robust object tracking with online multiple instance learning – volume: 100 start-page: 31 year: 2013 end-page: 40 ident: 11 article-title: Robust visual tracking based on online learning sparse representation – volume: 89 start-page: 31 issue: 1-2 year: 1997 end-page: 71 ident: 23 article-title: Solving the multiple instance problem with axis-parallel rectangles – reference: Grabner H. Leistner C. Bischof H. Semi-supervised on-line boosting for robust tracking Proceedings of the 10th European Conference on Computer Vision (ECCV '08) October 2008 Marseille, France 234 247 – reference: Xu X. Frank E. Dai H. Srikant R. Zhang C. Logistic regression and boosting for labeled bags of instances Proceedings of the 8th Pacific-Asia Conference, Advances in Knowledge Discovery and Data Mining Lecture Nodes in Computer Science (PAKDD '04) May 2004 Sydney, Australia 272 281 10.1007/978-3-540-24775-3_35 2-s2.0-7444219637 – reference: Galleguillos C. Babenko B. Rabinovich A. Belongie S. Weakly supervised object recognition and localization with stable segmentations Proceedings of the 10th European Conference on Computer Vision (ECCV '08) October 2008 Marseille, France – reference: Andrews S. Tsochantaridis I. Hofmann T. 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Snippet	An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance...
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SubjectTerms	Algorithms Ambiguity Artificial Intelligence Attention - physiology Classifiers Distance learning Eye Movements - physiology Feedback Histograms Humans Learning Learning - physiology Machine learning Mathematical research Methods Online Systems Photic Stimulation Probability Strategy Technology application Tracking Visual
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Title	Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm
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Volume	2016
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