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基于卡尔曼滤波器组的Mean Shift模板更新算法 被引量:20

An Algorithm of Mean Shift Template Update Based a Group of Kalman Filters
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摘要 针对Mean Shift算法缺乏必要的模板更新方法的缺陷,提出了一种基于卡尔曼滤波器组的Mean Shift模板更新算法。该算法首先将目标在特征空间中的特征值的概率作为模板信息;然后设计了一个滤波器组,其中每个滤波器用于估计特征子空间中一个子特征值概率的变化;最后将这些子特征值概率对应相乘就可以得到整个模板的更新值。由于滤波器的噪声参数是随着输入数据的变化随时动态确定的,因此,根据滤波器残差的变化就可以确定模板的更新策略。实验证明,该新算法不仅能够增强Mean Shift算法在目标姿态变化、光照变化下的跟踪效果,而且对阻挡时的鲁棒性也较好。 To improve the limitation of Mean Shift lacks method of template update, an algorithm of template update based a group of Kalman filters is proposed. Probability of elgenvalue in feature space is taken as the template information. A group of filters are devised, where each filter is used to estimate the change of probability of sub-eigenvalue. All update value of template can be received by multiplying these corresponding probabilities in sub-feature space. The noise parameter of each filter would change with input data, so a novel strategy of template update according to change of residual of filters could be proposed. Experimental results show that the proposed algorithm can successfully track target under condition of changeable gesture of target and changeable illumination, and is robust to occlusion.
出处 《中国图象图形学报》 CSCD 北大核心 2007年第3期460-465,共6页 Journal of Image and Graphics
关键词 Mean SHIFT算法 卡尔曼滤波器 模板更新 Mean Shift, Kalman filter, template update
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  • 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. 被引量:1
  • 2Cheng Y.Mean shift,mode seeking,and clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(8):790 -799. 被引量:1
  • 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. 被引量:1
  • 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. 被引量:1
  • 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. 被引量:1
  • 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. 被引量:1
  • 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. 被引量:1
  • 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. 被引量:1
  • 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. 被引量:1
  • 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. 被引量:1

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