一种融合多级稀疏表达和度量学习的目标跟踪方法

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.

A traget tracking method combining multi-level sparse representation and metric learning

Traget tracking methods based on the sparse representation mostly apply a template update strategy based on the fixed threshold which is difficult to adapt to the changing shape of target. In addition, sparse representation is inadequate in capturing the manifold structures hidden in target samples. A template update strategy based on the multi-level hierarchical dictionary is proposed according to drawbacks of the template update strategy based on the fixed threshold. A tracking method combining multi-level sparse representation and metric learning is proposed in order to improve the ability to distinguish between background and targets. Experimental results show that the proposed method can improve the tracking accuracy and robustness effectively.