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基于均值漂移和粒子滤波的红外目标跟踪 被引量:18

IR target tracking based on mean shift and particle filter
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摘要 为了提高红外目标跟踪的准确性和稳健性,提出了基于均值漂移(mean shift)和粒子滤波(PF)相结合的红外目标跟踪方法。在PF理论框架下,使用均值漂移为一种迭代模式寻找过程,对随机粒子样本进行重新分配,使粒子向目标状态的最大后验核密度估计方向移动,在均值漂移迭代过程中对样本权值进行更新。红外目标的状态后验概率分布用重新分配的加权随机样本集表示,对随机样本集使用PF算法实现红外目标运动的跟踪。实验结果表明,和一般PF和均值漂移相比,本文方法具有优越性和更强的稳健性。 A novel method for infrared target tracking which combines mean shift and particle filter was proposed in order to improve the tracking accuracy and robustness. Based on the particle filter,the mean shift was introduced as an iterative mode seeking procedure, in which particles move toward the maximal posterior kernel density estimation of target state. The weights of particle samples are updated as the mean shift iterative operating. The posterior distribution of the infrared target is approximated by a set of re-weighted samples,while the infrared target tracking is implemented by the particle filter algorithm which constructed by the sample set. Experimental results show that the proposed method is more effective and robust than the independent standard particle filter and mean shift.
出处 《光电子.激光》 EI CAS CSCD 北大核心 2008年第2期213-217,共5页 Journal of Optoelectronics·Laser
基金 国家自然科学基金重点资助项目(60634030) 国家自然科学基金资助项目(60602056) 遥感科学国家重点试验室开放基金资助项目(SK050013)
关键词 粒子滤波(PF) 均值漂移 核密度估计 红外目标跟踪 Bhattachryya系数 particle filter(PF) mean shift kernel density estimate IR target tracking Bhattachryya coefficient
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参考文献16

  • 1杨暤昀,张桂林.一种新的相关跟踪算法的设计与实现[J].红外与毫米波学报,2000,19(5):377-380. 被引量:26
  • 2李熙莹,倪国强.红外图像的光流计算[J].红外与激光工程,2002,31(3):189-193. 被引量:20
  • 3王维雅,丁雪梅,黄向东,谭久彬,李海英.一种小目标快速识别与跟踪方法[J].光电子.激光,2007,18(1):121-124. 被引量:14
  • 4许廷发,倪国强.基于LOG GABOR小波相位一致不变量的目标识别[J].光电子.激光,2006,17(2):222-225. 被引量:4
  • 5Yang C, Duraiswami R, Davis L. Efficient mean-shift tracking via a new similarity measure[A]. IEEE Cenf on Comp Vision and Patt Recog[C].2005,176-183.
  • 6Comaniciu D,Ramesh V, Meer P. Real-lime tracking of non-rigid objects using mean shift[A]. IEEE Conference on Computer Vision and Pattern recognition[C]. 2000,2:142-149.
  • 7Gordon N J,Salmond D J,Smith A F M. Novel approach to nonlinear/ non-Gaussian Bayesian state estimation[J]. IEEE Proceedings, 1993, 140(2) : 107-113.
  • 8Doucet A,Godsill S,Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and Computing, 2000, 10 (3) : 197-208.
  • 9Isard M, Blake A. Condensation-conditional density propagation for visual tracking[J]. International Journal of Computer Vision, 1998,29 (1) :5-28
  • 10Nummiaro K,Koller-Meier E,Gool L V.An adapative color-based particle filter[J].Image and Vision computing,2003,21(1):99-110.

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