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 l2 -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 l1 -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.