Efficient structured $$\ell 1$$ℓ1 tracker based on laplacian error distribution

Recently, sparse representation has been applied to visual tracking by casting the tracking problem into linear regression problem with sparse coefficient constraint. Under the Gaussian error distribution assumption, the reconstructed loss function is composed of sum-of-squares error term and $$\ell 1$$ℓ1 regularization, which is sensitive to the outliers caused by occlusion and local deformation. In this paper, we propose a robust and efficient $$\ell 1$$ℓ1 tracker based on laplacian error distribution and structured similarity regularization in a particle filter framework. Specifically, we model the error term by laplacian distribution, which has better robustness to the outliers than Gaussian distribution. Meanwhile, in contrast to most existing $$\ell 1$$ℓ1 trackers that handle particles independently, we exploit the dependence relationship between particles and impose the structured similarity regularization on the sparse coefficient set. The customized Inexact Augmented Lagrange Method (IALM) is derived to efficiently solve the optimization problem in batch mode. In addition, we also reveal that the proposed method is related to the robust regression with self-adaptive Huber loss function. Both the computational efficiency and tracking accuracy are enhanced by this novel cost function and optimization strategy. Qualitative and quantitative evaluations on the largest open benchmark video sequences show that our approach outperforms most state-of-the-art trackers.