基于Mean-Shift和粒子滤波的两步多目标跟踪方法
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重庆市科技攻关项目(7818);重庆市自然科学基金(CSTC,2005BB2063);重庆市教委科学技术项目(050509,060504,060517)


A two-step multiple targets tracking algorithm based on Mean-Shift and particle filter
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    摘要:

    针对Mean-Shift跟踪算法容易跟踪丢失以及粒子滤波跟踪算法计算量大等问题,提出了一种两步多目标跟踪算法。利用Mean-Shift进行第一步跟踪得到候选目标,在跟踪不准的情况下再利用粒子滤波进行后续的跟踪结果修正。实验结果表明两步跟踪算法既能保持Mean-Shift跟踪的实时性,也能维持粒子滤波跟踪算法的鲁棒性,有很高的实用性。

    Abstract:

    Since Mean-Shift (MS) tracking algorithm always loses tracking objects and the particle filter tracking algorithm costs huge computation, a novel two-step multiple targets tracking algorithm was proposed. Firstly, a candidate object was gotten by Mean-Shift (MS) tracking algorithm. Then, the tracking result would be verified by particle filter technique when the object couldn't be traced exactly. Experimental results show that this approach can maintain the efficiency of MS algorithm and the powerful ability of particle filter technique, so it is of high practicability.

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李红波,曾德龙,吴渝.基于Mean-Shift和粒子滤波的两步多目标跟踪方法[J].重庆邮电大学学报(自然科学版),2010,22(1):112-117.

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  • 收稿日期:2009-03-05
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