Multi-task l₀ gradient minimization for visual tracking

In most object tracking algorithms based on sparse representation, the optimization problem is often formulated as an l₁ or l₂ minimization problem, because its primal l₀-norm minimization problem is NP-hard. In this paper, a visual tracking method is proposed based upon l₀-norm minimization which directly seeks solution to the primal l₀ problem. To avoid solving a large number of l₀ minimization problems, we introduce to encode all samples simultaneously in a multi-task manner, which means that the number of minimization problem to be solved is only one, and an algorithm is presented to solve the minimization problem. Our tracking algorithm is then implemented under the framework of particle filter. Experiments on different challenging video sequences demonstrate that our method can achieve robust tracking results.