Rotation-aware dynamic temporal consistency with spatial sparsity correlation tracking
作者：
Highlights：
• Dynamic temporal modeling can make the filter highlight the ever-changing region.
• Dynamic temporal consistency modeling and spatial sparsity are incorporated in a unified optimization learning model.
• The connected hyper ellipse fitting strategy tries to achieve the optimal rotation angle.
• Comprehensive experiments have fully demonstrated the superior performance of our proposed method.
摘要
•Dynamic temporal modeling can make the filter highlight the ever-changing region.•Dynamic temporal consistency modeling and spatial sparsity are incorporated in a unified optimization learning model.•The connected hyper ellipse fitting strategy tries to achieve the optimal rotation angle.•Comprehensive experiments have fully demonstrated the superior performance of our proposed method.
论文关键词：Dynamic temporal consistency,Spatial sparsity,Correlation tracking,Rotation invariance,Connected hyper ellipse fitting
论文评审过程：Received 26 May 2022, Revised 8 August 2022, Accepted 26 August 2022, Available online 5 September 2022, Version of Record 19 September 2022.
论文官网地址：https://doi.org/10.1016/j.imavis.2022.104546
