A parallel search strategy based on sparse representation (PS-L1 tracker) is proposed in the particle filter framework. To obtain the weights of state particles, target templates are represented linearly with the dictionary of target candidates. Sparse constraints on the coefficient guarantee that only true target candidates can be selected, and the nonnegative entries denote the associate weights of efficient target states. Then the optimal target state can be estimated by the linear combination of above weighted states. In this way, efficient target states are selected simultaneously from all the particles, which we call a parallel search strategy. Experimental results demonstrate excellent performance of the proposed method on challenging infrared images.