Visual tracking via sparsity pattern learning

Recently sparse representation has been applied to visual tracking by modeling the target appearance using a sparse approximation over the template set. However, this approach is limited by the high computational cost of the ℓ₁-norm minimization involved, which also impacts on the amount of particle samples that we can have. This paper introduces a basic constraint on the self-representation of the target set. The sparsity pattern in the self-representation allows us to recover the 'sparse coefficients' of the candidate samples by some small-scale ℓ₂-norm minimization; this results in a fast tracking algorithm. It also leads to a principled dictionary update mechanism which is crucial for good performance. Experiments on a recently released benchmark with 50 challenging video sequences show significant runtime efficiency and tracking accuracy achieved by the proposed algorithm.