Robust visual tracking via spatio-temporal adaptive and channel selective correlation filters
作者：
Highlights：
• A Taylor expansion based criterion is proposed to choose a few channels of target-specific features.
• An elastic net regularizer is introduced into filter learning to select and group features inside the target bounding box.
• A fast filter transformation algorithm is proposed to constrain the filters to be temporally adaptive.
• An efficient solution is developed to optimize the filters, and the tracker is evaluated on six popular datasets to show its state-of-the-art performance.
摘要
•A Taylor expansion based criterion is proposed to choose a few channels of target-specific features.•An elastic net regularizer is introduced into filter learning to select and group features inside the target bounding box.•A fast filter transformation algorithm is proposed to constrain the filters to be temporally adaptive.•An efficient solution is developed to optimize the filters, and the tracker is evaluated on six popular datasets to show its state-of-the-art performance.
论文关键词：Visual tracking,Correlation filter,Filter compression,Elastic net regression,Filter transformation
论文评审过程：Received 18 February 2020, Revised 14 October 2020, Accepted 29 October 2020, Available online 29 October 2020, Version of Record 30 January 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107738
