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混合目标模型的Mean Shift跟踪算法 被引量：1

Mean Shift tracking algorithm with hybrid target model
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摘要 但固定目标模型的Mean Shift算法采用直方图进行匹配,而直方图是一种比较弱的目标特征,当背景和目标的颜色分布较相似时其跟踪效果欠佳。针对这一缺点,提出了一种采用混合目标模型的Mean Shift算法。该算法在匹配过程中使用的目标模型包含了初始帧和前一帧的信息,克服了固定目标模型难以对与背景相似目标以及旋转目标进行准确描述的缺点,获得了较好的跟踪效果。 As Mean Shift algorithms with unchanged target model matching candidate by histogram,the histogram is a relative weak characteristic,the accurateness of tracking is unsatisfactory when the background and the target have similar color attribute.To improve the limitation of Mean Shift,a modified algorithm with hybrid target model is proposed.The hybrid target model takes into account the information of the first frame and the previous frame,thus overcomes the weakness of unchanging target model that can not accurately represent the rotate target and the target that has similar color with background.Experimental results show that the proposed algorithm can achieve better effect.
作者 黎云汉 楼京京
机构地区 义乌工商职业技术学院计算机工程系
出处 《计算机工程与应用》 CSCD 北大核心 2010年第16期200-203,共4页 Computer Engineering and Applications
关键词 混合目标模型 Mean SHIFT 目标跟踪 hybrid target model Mean Shift object tracking
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献12

  • 1Fukunaga K,Hostetler L.The estimation of the gradient of a density function,with applications in pattern recognition[J].IEEE Transactions on Information Theory,1975,21 (1):32-40.
  • 2Cheng Y,Mean shift,mode seeking,and clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17 (8):790-799.
  • 3Comaniciu D,Ramesh V,Meer P.Real-time tracking of non-rigid objects using mean shif[C] //Procecdings of IEEE Conference on Computer Vision and Pattern Recognition,2000.
  • 4Comaniciu D,Meer P.Mean shift:A robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
  • 5Comaniciu D,Ramesh V,Meer P.Kernel-based object tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25 (5):564-577.
  • 6Comaniciu D,Ramesh V,Meer P.The variable bandwidth mean shift and data-driven scale selection[C] //Procecdings of Eighth IEEE International Conference on Computer Vision,ICCV 2001,Vancouver,BC,Canada,2001.
  • 7Jia Jing-ping,Zhan Rong-chun.Tracking of objects in image sequences using bandwidth matrix mean shift algorithm[C] //Proeeedings of 7th International Conference on Signal Processing,ICSP'04,2004.
  • 8Chen X.Adaptive bandwidth Mean Shift object tracking[C] //IEEE Conference on Robotics,Automation and Mechatrnnics,2008.
  • 9Jiang Zhuo-lin,Li Shao-fa,Jia Xi-ping,et al.An improved mean shift tracking method based on nonparametric clustering and adaptive bandwidth[C] //2008 International Conference on Machine Learning and Cybernetics,2008.
  • 10Collins R T.Mean-shift blob tracking through scale space[C] //Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recegnition,2003.

二级参考文献11

  • 1Fukunage K,Hostetler L D.The estimation of the gradient of a density function with application in pattern recognition[J].IEEE Transactions of Information Theory,1975,21 (1):32 - 40.
  • 2Cheng Y.Mean shift,mode seeking,and clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(8):790 -799.
  • 3Comaniciu D,Meer P.Mean shift analysis and application[A].In:Proceedings of the Seventh IEEE International Conference on Computer Vison[C],Washingtan,DC,USA,1992,2:1197-1203.
  • 4Comaniciu D,Meer P.Mean shift:A robust application toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603 - 619.
  • 5Comaniciu D,Meer P.robust analysis of feature spaces:color Image Segmentation[A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)[C],San Juan,Puerto Rico,1997:750 -755.
  • 6Comaniciu D,Ramesh V,Meer P.The variable bandwidth Mean shift and data-driven scale selection[A].In:Proceedings of IEEE International Conference on Computer Vision (ICCV'01)[C],Vancouver,Canada,2001,1:438 -445.
  • 7Yang Changjiang.Efficent Mean-Shift tracking via a new similarity measure[A].In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)[C],San Diego,California,USA,2005,1:176- 183.
  • 8Collins R T.Mean-Shift blob tracking through scale space[A].In:Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'03)[C],Madison,Wisconsin,USA,2003,2:234 - 240.
  • 9Nummiaro K,Koller-Meier E,Van Goo L.Color features for tracking non-rigid objects.Special Issue on Visual Surveillance[J].Chinese Journal of Automation,2003,29 (3):345 - 355.
  • 10Nguyen H T,Worring M,Rein van den Boomgaard.Occlusion robust adaptive template tracking[A].In:Proceedings of Eighth International Conference on Computer Vision (ICCV'01)[C],Vancouver,British Columbia,Canada,2001,1:678 -683.

共引文献19

同被引文献13

  • 1Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift[ C ]. Computer Vision and Pattern Recognition, 2000 Proceedings IEEE Conference on,2000.
  • 2Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking [ J ]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2003, 25(5) :564-577.
  • 3Collins R T. Mean-shift blob tracking through scale space [ C ]. Madison, WJ: Computer Vision and Pattern Recognition. IEEE Computer Society Conference on,2003.
  • 4Debeir O, et al. Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes [ J ]. Medical Imaging, IEEE Transactions on, 2005, 24(6) :697-711.
  • 5Peihua L. An adaptive binning color model for mean shift tracking [J]. Circuits and Systems for Video Technology, IEEE Transactions on, 2008, 18 ( 9 ) : 1293-1299.
  • 6Li Z, Tang Q L, Sang N. Improved mean shift algorithm for occlusion pedestrian tracking[ J ]. Electronics Letters, 2008, 44 ( 10 ) : 622-623.
  • 7Junqiu W, Yagi Y. Adaptive mean-shift tracking with auxiliary particles[J]. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 2009, 39 (6) : 1578-1589.
  • 8Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis [ J ]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002, 24(5) :603-619.
  • 9Lowe D G. Object recognition from local scale-invariant features [ C ]. Kerkyra, Greece : Computer Vision, the Seventh IEEE International Conference on, 1999.
  • 10Lowe D G. Distinctive image features from scale-invariant keypoints [ J]. International Journal of Computer Vision 2004, 60 ( 2 ) : 91- 110.

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计算机工程与应用
2010年 第16期

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