Learning Adaptive Sparse Spatially-Regularized Correlation Filters for Visual Tracking
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 l_{2}-norm, which can make the trained model noisy and not sparse. As a result, we propose a mo...
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Veröffentlicht in: | IEEE signal processing letters 2023-01, Vol.30, p.1-5 |
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Sprache: | eng |
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Zusammenfassung: | 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 l_{2}-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 l_{1}-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. |
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ISSN: | 1070-9908 1558-2361 |
DOI: | 10.1109/LSP.2023.3238277 |