Abstract In this paper, we propose a robust visual tracking method based on mutual kernelized correlation filters with elastic net constraint. First, two correlation filters are trained in a general framework jointly in a closed form, which are interrelated and interacted on each other. Second, elastic net constraint is imposed on each discriminative filter, which is able to filter some interfering features. Third, scale estimation and target re-detection scheme are adopted in our framework, which can deal with scale variation and tracking failure effectively. Extensive experiments on some challenging tracking benchmarks demonstrate that our proposed method is able to obtain a competitive tracking performance against other state-of-the-art algorithms.
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