Learn++ for Robust Object Tracking
Learn++ for Robust Object Tracking
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DOI：
10.5244/c.28.28
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Feng Zheng;Ling Shao;J. Brownjohn;V. Racic
中科院分区：
文献类型：
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作者：
Feng Zheng;Ling Shao;J. Brownjohn;V. Racic
在本文中，一个学习++（LPP）跟踪器提出了有效地选择特定的分类器的鲁棒性和长期的对象跟踪。与以前的在线方法相比，LPP跟踪器动态地维护一组基本分类器，这些分类器在不访问原始数据的情况下顺序训练，但保留了先前获得的知识。该方法可以指定不同的基本分类器子集，以解决非平稳环境中出现的不同子问题。因此，一个最佳的分类器可以近似在一个活跃的子空间所跨越的选定的自适应基本分类器。因此，LPP跟踪器可以根据样本的分布和最近的性能，通过自动调整活动子集并在子集所覆盖的活动子空间中搜索最优分类器来解决“概念漂移”问题。实验结果表明，LPP跟踪器在各种具有挑战性的环境条件下都具有最先进的性能，特别是可以同时克服几个挑战。
In this paper, a Learn++ (LPP) tracker is proposed to efficiently select specific classifiers for robust and long-term object tracking. In contrast to previous online methods, LPP tracker dynamically maintains a set of basic classifiers which are trained sequentially without accessing original data but preserving the previously acquired knowledge. The different subsets of basic classifiers can be specified to solve different sub-problems occurred in a non-stationary environment. Thus, an optimal classifier can be approximated in an active subspace spanned by selected adaptive basic classifiers. As a result, LPP tracker can address the “concept drift”, by automatically adjusting the active subset and searching the optimal classifier in an active subspace spanned by the subset according to the distribution of the samples and recent performance. Experimental results show that LPP tracker yields state-of-the-art performance under various challenging environmental conditions and, especially, can overcome several challenges simultaneously.