Visual tracking using spatio-temporally nonlocally regularized correlation filter

作者：

Highlights：

• A novel regularized CF based tracking approach has been proposed with promising results on three benchmark datasets.

• Our method effectively captures the long-term spatio-temporally nonlocal superpixel appearance information to regularize the CF learning.

• Our method deals well with the challenging factors such as large viewpoint changes and non-rigid deformation.

摘要

•A novel regularized CF based tracking approach has been proposed with promising results on three benchmark datasets.•Our method effectively captures the long-term spatio-temporally nonlocal superpixel appearance information to regularize the CF learning.•Our method deals well with the challenging factors such as large viewpoint changes and non-rigid deformation.

论文关键词：Visual tracking,Video segmentation,Nonlocal appearance learning,Graphical model,Optical flow

论文评审过程：Received 12 January 2018, Revised 5 March 2018, Accepted 20 May 2018, Available online 29 May 2018, Version of Record 4 June 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.05.017