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基于在线多示例学习的目标跟踪 被引量：2

Object Tracking with Online Multiple Instance Learning
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摘要 分析了多示例学习法,研究了在线多示例学习目标跟踪技术的应用,对现有的一些方法进行了比较,并指出了今后多示例学习目标跟踪的研究方向. A new learing paradigm,namely Multiple Instance Learning and the application of online MIL in object tracking are studied as well.Different approaches are compared and the research trend of multiple instance learning are predicted.
作者 徐曼
机构地区 上海电力学院计算机与信息工程学院 复旦大学计算机科学技术学院
出处 《上海电力学院学报》 CAS 2011年第1期42-44,共3页 Journal of Shanghai University of Electric Power
关键词 在线学习 多示例学习 目标跟踪 online learning multiple instance learning object tracking
分类号 TN820.4 [电子电信—信息与通信工程]
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参考文献12

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同被引文献25

  • 1BradskiG,KaeblerA.学习OpenCV(中文版)[M].于仕琪,刘瑞帧,译.北京:清华大学出版社,2009.
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  • 3VIOLA P, PLATI" J, ZHANG C. Multiple instance Boosting for ob- ject detection[J].Advances in Neural Information Processing Sys- tems, 2006(18) : 1417-1424.
  • 4CALONDER M, LEPETIT V, FUA P. Fast kyepoint recognition us- ing random ferns[J]. IEEE Trans. Pattern Analysis and Machine In- telligence, 2010,32 ( 3 ) : 448-461.
  • 5BABENKO B,YANG M H, BELONGIE S.VisUAL tracking with on- line multiple instance learning[C]//Proc. CVPR 2009.[S.1.]: IEEE Press, 2009: 983-990.
  • 6QI Zhiquan, XU Yitian, WANG Laisheng. Online multiple instance Boosting for object detection[J]. Neurocomputing, 2011,74 (10) : 1769-1775.
  • 7GRABNER H, BISCHOF H. On-line Boosting and vision[C]//Proc. CVPR 2006. New York :IEEE Press, 2006: 260-267.
  • 8李雅林,张化祥,张顺.基于近邻加权及多示例的多标记学习改进算法[EBIOL].[2012-10-28].http://www.cnki-net/kcms/detail/11.2127.rP.20120801.1653.030.html.
  • 9GRABNER H, LEISTNER C, BISCHOF H. Semi-supervised on-line Boosting for robust tracking [C]// Proc. European Conf on Computer Vision.Berlin: Springer, 2008 : 234-247.
  • 10GRABNER H,GRABNER M,BISCHOF H. Real-time tracking via on-line boosting[C]//Proc. British Machine Vision Conference.Edin- burgh: [s.n.], 2006 : 27-36.

引证文献2

二级引证文献14

上海电力学院学报
2011年 第1期

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