Multibeam Forward-Looking Sonar Video Object Tracking Using Truncated - Sparsity and Aberrances Repression Regularization

Xueqiong Sui, Han Pan, Zhongliang Jing, Henry Leung

Abstract: Multibeam forward-looking sonar (MFLS) video object tracking is a challenging problem due to the negative impacts of weak features and background clutter. In this letter, a novel multibeam forward-looking sonar video object tracking method via hybrid regularization scheme is proposed. The proposed regularization scheme is a composite method with truncated $\ell _{1}$-$\ell _{2}$ sparsity regularization and aberrances repression regularization. While the truncated $\ell _{1}$-$\ell _{2}$ sparsity regularization explores the structural sparsity of the learned filter to address background clutter, the aberrances repression regularization can alleviate the undesired spatial bounding effect. The resulting optimization problem is solved by alternating direction method of multipliers (ADMM). A proximal operator with truncated soft-thresholding scheme is proposed for the sub-problem with truncated $\ell _{1}$-$\ell _{2}$ sparsity regularization. Experiments based on five multibeam forward-looking sonar videos for underwater docking validate the effectiveness of the proposed method, compared to other 8 state-of-the-art tracking methods.