Abstract:
This paper proposes a novel Importance Guided Sparse Spatio-Temporal Regularization based Correlation Filter (IGSSTRCF) tracker. Our formulation explicitly models the variations in the correlation filters and associated spatial weights in successive frames. By imposing a sparsity penalty on these variations, the formulation ensures that only relevant changes are incorporated during updates. This results in more robust filter coefficients that minimize the tracking drift. The IGSSTRCF also includes an adaptive channel importance estimation strategy that assigns an importance weight to each feature channel during training. The proposed formulation is efficiently solved via the alternating direction method of multipliers. A comparative analysis is shown on TC128, UAV123, VOT-2017, and VOT-2019 datasets; and we present an ablation study to demonstrate the contribution of each component of the IGSSTRCF. It is observed that we outperform several state-of-the-art trackers and each component of the proposed IGSSTRCF contributes positively towards tracker performance.
