  In this paper, we propose a novel sparse coding and counting method under
Bayesian framwork for visual tracking. In contrast to existing methods, the
proposed method employs the combination of L0 and L1 norm to regularize the
linear coefficients of incrementally updated linear basis. The sparsity
constraint enables the tracker to effectively handle difficult challenges, such
as occlusion or image corruption. To achieve realtime processing, we propose a
fast and efficient numerical algorithm for solving the proposed model. Although
it is an NP-hard problem, the proposed accelerated proximal gradient (APG)
approach is guaranteed to converge to a solution quickly. Besides, we provide a
closed solution of combining L0 and L1 regularized representation to obtain
better sparsity. Experimental results on challenging video sequences
demonstrate that the proposed method achieves state-of-the-art results both in
accuracy and speed.
