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Learning Adaptive Sparse Spatially-Regularized Correlation Filters for Visual Tracking
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AI
DOI：10.1109/LSP.2023.3238277.png)
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摘要
En 中文
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.
Keyword:
Visual object tracking
discriminative correlation filter
elastic net regression
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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