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基于在线学习和结构约束的目标跟踪算法 被引量：2

Target Tracking Algorithm Based on Online Learning and Construction Constraint
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摘要 为在复杂环境中对目标进行长期的精确跟踪,提出一种基于在线学习和结构约束的目标检测和跟踪算法。采用改进的光流法对特定目标进行自适应跟踪,实时目标检测采用非层次结构在线学习随机蕨丛分类器。用基于结构约束的非监督学习法精确确定目标位置,以适应目标的形态变化。实验结果表明,该算法能够适应目标的基本形态变化,在目标出现尺寸变化、旋转、部分遮挡或短暂消失时都能稳定精确地跟踪目标。 In order to accurately track target during a long term in complex environment,this paper presents an algorithm combining detecting and tracking based on online learning and construction constraint.Improved optical flow method is used to adaptively track specific target.The nonhierarchical structure online learning random ferns classifier is used as real time target detecting method.In order to adapt target shape variation,unsupervised learning method based on construction constraint is used to accurately determine the target position.Experimental results show that this algorithm can adapt to the basic target shape variations and track the target steadily when the targets change in size,rotate,shelter partly or disappear in a short term.
作者 王守超 李小霞
机构地区 西南科技大学信息工程学院
出处 《计算机工程》 CAS CSCD 2012年第18期140-143,共4页 Computer Engineering
基金 国家自然科学基金资助项目"单目高精度大型物体彩色三维数字化测量原理研究"(60808020)
关键词 随机蕨丛 结构约束 光流法 在线学习 目标跟踪 前向-后向误差 random ferns brake； construction constraint； optical flow method； online learning； target tracking； forward-backward error
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献8

  • 1Oza N C, Russell S. Online Bagging and Boosting[C]//Proc. of Artificial Intelligence and Statistics. San Francisco, USA: Morgan Kaufmann, 2001.
  • 2Grabner H, Bischof H. On-line Boosting and Vision[C]//Proc. of IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: [s. n.], 2006.
  • 3Tang Feng, Brennan S, Zhao Qi, et a1. Co-tracking Using Semi-supervised Support Vector Machines[C]//Proc. of IEEE Conference on Computer Vision. Rio de Janeiro, Brazil: [s. n.], 2007.
  • 4Kalal Z, Mikolajczyk K, Matas J. Online Learning of Robust Object Detectors During Unstable Tracking[C]//Proc. of IEEE Conference on Computer Vision. Kyoto, Japan: [s. n.], 2009.
  • 5齐志泉,宋野,王来生.基于在线学习的目标跟踪方法研究[J].计算机应用研究,2010,27(2):770-771. 被引量：4
  • 6Ozuysal M, Calonder M, Lepetit V, et al. Fast Keypoint Recognition Using Random Ferns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(3): 448-461.
  • 7Kalal Z, Mikolajczyk K, Matas J. Forword-Backward Error: Automatic Detection of Tracking Failures[C]//Proc. of International Conference on Pattern Recognition. Istambul, Turkey: [s. n.], 2010.
  • 8Kalal Z, Mikolajczyk K, Matas J. P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints[C]//Proc. of IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE Press, 2010.

二级参考文献11

  • 1AVIDAN S. Ensemble tracking [ C ]// Proc of CVPR. 2005 : 494-501.
  • 2LEPETIT V, LAGGER P, FUA P. Randomized trees for real-time keypoint recognition[ C]//Proc of CVPR. 2005 : 775-781.
  • 3GRABNER H, BISCHOF H. On-line Boosting and vision [ C ]//Proc of CVPR. 2006 : 260-267.
  • 4GRABNER M,GRABNER H,BISCHOF H. Learning features for tracking [C]//Proc of CVPR. 2007:1-8.
  • 5GRABNER H, SOCHMAN J, BISCHOF H, et al. Training sequential on-line Boosting classifier for visual tracking [ C ]//Proc of ICPR. 2008 : 1-4.
  • 6BLUM A, MITCHELL T. Combining labeled and unlabeled data with co-training [ C]//Proc of the llth Annual Conference on Computational Learning Theory. New York :ACM Press, 1998:92-100.
  • 7COLLINS M,SINGER Y. Unsupervised models for named entity classification[ C]//Proc of Empirical Methods in Natural Language Processing. 1999.
  • 8JAVED O,ALI S, SHAH M. On-line detection and classification of moving objects using progressively improving detectors [ C ]//Proc of CVPR. 2005 : 695-700.
  • 9OZA N C. On-line ensemble learning [ D ]. Berkeley: University of Califormia, 2002.
  • 10FRIEDMAN J, HASTIE T,TIBSHIRANI R. Additive logistic regression: a statistical view of Boosting[ J]. Annals of Statistics, 2000, 28(2) :337-407.

共引文献3

同被引文献19

  • 1Cannons K J,Gryn J M, Wildes R P. Visual Tracking Using a Pixelwise Spatiotemporal Oriented Energy Representation : C 3//Proceedings of the 11 th European Conference on Computer Vision. New York,USA : ACM Press ,2010:511-524.
  • 2Liu Tianjian, Zhang Zutao. Adaptive Double Kalman Filter and Mean Shift for Robust Fast Object Track- ing[ J]. International Journal of Advancements in Comoutinz Technololzv .2013.5 ( 6 ) :349-356.
  • 3Stalder S, Grabner H, van Gool L. Beyond Semi- supervised Tracking: Tracking Should Be as Simple as Detection, but Not Simpler than Recognition [ C ]// Proceedings of IEEE International Conference on Computer Vision Workshops. Washington D. C. , USA: IEEE Press ,2009 : 1409-1416.
  • 4Ning Jifeng, Shi Wuzhen, Yang Shuqin, et al. Visual Tracking with Online Multiple Instance Learning [ J ]. Image and Vision Computing ,2009,31 ( 11 ) :983-990.
  • 5Kalal Z, Matas J, Mikolajczyk K. P-N Learning: Bootstrapping Binary Classifiers by Structural Con- straints [ C 1//Proceedings of IEEE Conference on Com- puter Vision and Pattern Recognition. Washington D. C., USA :IEEE Press,2010:49-56.
  • 6Szeliski R. Image Alignment and Stitching: A TutorialE J:. Foundations and Trends in Computer Graphics and Vision ,2006,2 ( 1 ) : 101-104.
  • 7Ning Jifeng, Zhang Lei. Robust Mean-shift Tracking with Corrected Background-weighted Histogram E J]. IET Computer Vision,2012,6( 1 ) :62-69.
  • 8Mobahi H, Zitnick C L, Ma Y. Seeing Through the Blur:C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press ,2012 : 1736-1743.
  • 9Dalal N,Triggs B. Histograms of Oriented Gradients for Human Detection E C ]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA : IEEE Press,2005 : 886-893.
  • 10Sevilla-lara L, Learned-miller E. Distribution Fields for Tracking I J ]. IEEE Conference on Computer Vision & Pattern Recognition ,2012,157 (10) : 1910-1917.

引证文献2

二级引证文献5

计算机工程
2012年 第18期

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