Regularisation learning of correlation filters for robust visual tracking

Recently, kernelised correlation filter (KCF)-based trackers aroused increasing interest and achieved extremely compelling results in different competitions and benchmarks in the field of visual object tracking. However, the training mechanism of the KCF that exploits simple linear combinations of filter from the previous frame easily cause error accumulation. To overcome this problem, the authors propose a novel training strategy that utilises all of the previous training samples, and a sparsity-related loss function regularised by the L1 norm to deal with the problem of the fixed template size in KCF trackers, a separate scale filter is learned for scale estimation during the tracking process. Moreover, powerful features that include histogram of oriented gradients (HOG) and colour features are integrated to further improve the robustness of the authors' tracking. Extensive experiments in various challenging situations demonstrate that the proposed method performs favourably against several state-of-the-art tracking algorithms.