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在线机器学习跟踪算法的研究进展

DOI: 10.11830/ISSN.1000-5013.2014.01.0041

Keywords: 目标跟踪算法, 在线机器学习, 目标漂移, 多跟踪器

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Abstract:

分类介绍在线机器学习跟踪算法的研究现状,比较各种算法的优缺点.研究表明:每一种跟踪算法都有其自身的优点和缺点,通常情况下只能处理某一些特定类型的变化,很难确保某一特定类型的跟踪算法能够处理复杂跟踪场景中的所有不确定因素.最后,针对在线学习算法容易产生误差积累,最终发生目标漂移的问题,提出使用多跟踪器的融合,实现鲁棒跟踪等相应的解决方案.

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