Abstract: Video object tracking is a fundamental problem in computer vision. Recently, due to the high efficiency and robustness of the discriminative correlation filter (DCF) , many DCF-based object tracking algorithms have emerged. In order to overcome the incompatibility of DCF tracker to motion blur object, this paper proposes a new algorithm that exploits Lasso constraints and integrates optical flow. First, the optical flow is integrated into the proposed tracker. Secondly, the objective function of DCF was constrained by Lasso. Consider the optimization efficiency of the constrained objective function, the block coordinate descent algorithm is used to optimize the constrained objective function. Compared with the DCF-based algorithms, the experimental results show that our algorithm can effectively track motion blur object and achieve robust tracking in complex environments.