An accelerated correlation filter tracker
作者：
Highlights：
• A formulation of the DCF design problem which focuses on informative feature channels and spatial structures by means of novel regularisation.
• A proposed relaxed optimisation algorithm referred to as R_A-ADMM for optimising the regularised DCF. In contrast with the standard ADMM, the algorithm achieves a better convergence rate.
• A temporal smoothness constraint, implemented by an adaptive initialisation mechanism, to achieve further speed up via transfer learning among video frames.
• The proposed adoption of AlexNet to construct a light-weight deep representation with a tracking accuracy comparable to more complicated deep networks, such as VGG and ResNet.
• An extensive evaluation of the proposed methodology on several well-known visual object tracking datasets, with the results confirming the acceleration gains for the regularised DCF paradigm.
摘要
•A formulation of the DCF design problem which focuses on informative feature channels and spatial structures by means of novel regularisation.•A proposed relaxed optimisation algorithm referred to as R_A-ADMM for optimising the regularised DCF. In contrast with the standard ADMM, the algorithm achieves a better convergence rate.•A temporal smoothness constraint, implemented by an adaptive initialisation mechanism, to achieve further speed up via transfer learning among video frames.•The proposed adoption of AlexNet to construct a light-weight deep representation with a tracking accuracy comparable to more complicated deep networks, such as VGG and ResNet.•An extensive evaluation of the proposed methodology on several well-known visual object tracking datasets, with the results confirming the acceleration gains for the regularised DCF paradigm.
论文关键词：Visual object tracking,Discriminative correlation filters,Accelerated optimisation,Alternating direction method of multipliers
论文评审过程：Received 14 May 2019, Revised 19 October 2019, Accepted 15 December 2019, Available online 24 December 2019, Version of Record 26 February 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107172
