Visual tracking via sparsity pattern learning

Yuxi Wang, Yue Liu, Zhuwen Li, Loong-Fah Cheong, Haibin Ling

Abstract: 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 ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -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 ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -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.