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稀疏表示的Lucas-Kanade目标跟踪

DOI: 10.11834/jig.20130306

Keywords: 稀疏表示,LK图像配准算法,视觉跟踪,L1范数最小化

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Abstract:

提出一种新的目标跟踪算法,将稀疏表示应用于LK(Lucas-Kanade)图像配准框架。通过最小化校准误差的L1范数来求解目标的状态参数,从而实现对目标的准确跟踪。对目标同时建立两个外观模型:动态字典和静态模板,其中动态模型由动态字典的稀疏表示来描述目标外观。为了解决由于动态字典不断更新造成的跟踪漂移问题,一个两阶段迭代机制被采用。两个阶段所采用的目标模型分别为动态字典和静态模板。大量的实验结果表明,本文算法能有效应对外观变化、局部遮挡、光照变化等挑战,同时具有较好的实时性。

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