Visual tracking based on online sparse feature learning
作者：
Highlights：
• Proposed an online sparse feature selection method for modeling tracking target from its neighboring background.
• Introduced a correlation based feature updating strategy to accommodate significant appearance change of the target
• Achieved more stable and accurate tracking results compared to several state-of-the-art methods
• Real-time processing speed
摘要
•Proposed an online sparse feature selection method for modeling tracking target from its neighboring background.•Introduced a correlation based feature updating strategy to accommodate significant appearance change of the target•Achieved more stable and accurate tracking results compared to several state-of-the-art methods•Real-time processing speed
论文关键词：Visual tracking,Sparse coding,Sparse feature,Bayesian classifier,Haar-like features
论文评审过程：Received 17 July 2014, Revised 19 December 2014, Accepted 2 April 2015, Available online 23 April 2015, Version of Record 15 May 2015.
论文官网地址：https://doi.org/10.1016/j.imavis.2015.04.005
