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一种融合多级稀疏表达和度量学习的目标跟踪方法

DOI: 10.13195/j.kzyjc.2014.1072, PP. 1791-1796

Keywords: 目标跟踪,稀疏表达,度量学习

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Abstract:

基于稀疏表达的跟踪方法通常采用基于固定阈值的模板更新策略,很难适应不断变化的目标外形;其次,稀疏表达缺乏描述目标流行结构的能力,区分背景和目标的能力差.针对基于固定阈值的模板更新策略的不足,提出一种多级分层的目标模板字典.为了改善对背景和目标的区分能力,提出一种融合多级稀疏表达和度量学习的目标跟踪方法.实验结果表明了所提出的方法能有效提高跟踪的鲁棒性和精度.

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