作者: Li, Xi; Shen, Chunhua; Dick, Anthony; 等.
来源：IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 卷: 38 期: 5 页: 931-950 出版年:MAY 2016
We propose a visual tracker based on a metric-weighted linear representation of appearance. To capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We design a time-weighted reservoir sampling method. We demonstrate the effectiveness of the method for both inter-frame tracking and object identification.
