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融合残差Unscented粒子滤波和区别性稀疏表示的鲁棒目标跟踪

DOI: 10.11834/jig.20140511

Keywords: 目标跟踪,Unscented粒子滤波,稀疏表示,动态模板更新,可变方向乘子法(ADMM)

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Abstract:

目的提出一种鲁棒的目标跟踪算法,将区别性稀疏表示模型应用于残差Unscented粒子滤波(RUPF)跟踪框架,从而实现对目标高效准确的跟踪。方法利用Unscented卡尔曼(UKF)滤波技术将目标的量测信息引入提议分布,并使用马尔可夫蒙特卡洛(MCMC)移动改进采样结果,提高了滤波的精度,同时有效防止了粒子的退化和贫化。基于稀疏表示建立区别性的目标观测模型,引入的背景成分可以增强算法分辨目标与背景的能力。采用可变方向乘子法(ADMM)解决稀疏表示中的L1优化问题,有效地提升了算法的执行效率。结果通过和其他跟踪算法一起,对标准测试视频进行的大量定性与定量的实验,结果表明,本文跟踪算法的跟踪精度高于一些常见的跟踪算法,同时其时间复杂度低于传统的几种基于稀疏的跟踪算法。结论随着硬件技术的不断发展,UKF滤波技术的速度不断提升,保证了本文算法可以在较高准确率下有更快的执行速度。

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