Extended compressed tracking via random projection based on MSERs and online LS-SVM learning

作者：

Highlights：

• A more stable and robust approach is proposed for visual tracking relying on Maximally Stable Extremal Regions (MSERs), sparse random projection and online Least Squares SVM classifier (LS-SVM) learning.

• With the fusion of MSERs and sparse random projection, the stable adaptive object appearance is modeled to adapt the variation of appearance.

• An online closed-form LS-SVM is employed to quickly and robustly predict the target object location.

摘要

Highlights•A more stable and robust approach is proposed for visual tracking relying on Maximally Stable Extremal Regions (MSERs), sparse random projection and online Least Squares SVM classifier (LS-SVM) learning.•With the fusion of MSERs and sparse random projection, the stable adaptive object appearance is modeled to adapt the variation of appearance.•An online closed-form LS-SVM is employed to quickly and robustly predict the target object location.

论文关键词：Visual tracking,Maximally stable extremal regions,Random projection,Online LS-SVM learning

论文评审过程：Received 30 August 2015, Revised 8 February 2016, Accepted 18 February 2016, Available online 27 February 2016, Version of Record 23 August 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.02.012