Recent visual object tracking methods have witnessed a continuous improvement
in the state-of-the-art with the development of efficient discriminative
correlation filters (DCF) and robust deep neural network features. Despite the
outstanding performance achieved by the above combination, existing advanced
trackers suffer from the burden of high computational complexity of the deep
feature extraction and online model learning. We propose an accelerated ADMM
optimisation method obtained by adding a momentum to the optimisation sequence
iterates, and by relaxing the impact of the error between DCF parameters and
their norm. The proposed optimisation method is applied to an innovative
formulation of the DCF design, which seeks the most discriminative spatially
regularised feature channels. A further speed up is achieved by an adaptive
initialisation of the filter optimisation process. The significantly increased
convergence of the DCF filter is demonstrated by establishing the optimisation
process equivalence with a continuous dynamical system for which the
convergence properties can readily be derived. The experimental results
obtained on several well-known benchmarking datasets demonstrate the efficiency
and robustness of the proposed ACFT method, with a tracking accuracy comparable
to the start-of-the-art trackers