Abstract
•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.