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- W4317496114 abstract "The correlation filter(CF)-based tracker is a classic and effective model in the field of visual tracking. For a long time, most CF-based trackers solved filters using only ridge regression equations with <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$l_{2}$</tex-math></inline-formula> -norm, which can make the trained model noisy and not sparse. As a result, we propose a model of adaptive sparse spatially-regularized correlation filters (AS2RCF). Aiming to suppress the noise mixed in the model, we improve it by introducing an <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$l_{1}$</tex-math></inline-formula> -norm spatial regularization term. This converts the original ridge regression equation into an Elastic Net regression, which allows the filter to have a certain sparsity while maintaining the stability of model optimization. The entire AS2RCF model is optimized using alternating direction method of multipliers(ADMM), and quantitative evaluations through extensive experiments on OTB-2015, TC128 and UAV123 demonstrate the tracker's effectiveness." @default.
- W4317496114 created "2023-01-20" @default.
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- W4317496114 date "2023-01-01" @default.
- W4317496114 modified "2023-10-16" @default.
- W4317496114 title "Learning Adaptive Sparse Spatially-Regularized Correlation Filters for Visual Tracking" @default.
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- W4317496114 doi "https://doi.org/10.1109/lsp.2023.3238277" @default.
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