To improve the accuracy and robustness of the target tracking algorithm under illumination changes, a joint optimization algorithm combining illumination compensation with multi-task sparse representation is proposed based on the sparse representation theory.First,the algorithm compensates for the illumination of the template according to the average brightness difference between the template and the candidate target.Then,the candidate target is used to construct an over-complete dictionary to represent the template after illumination compensation,and the problem is transformed into a multi-task optimization problem.Moreover,the sparse coding matrix is used to quickly eliminate unrelated candidates.Finally,based on the reconfiguration error,a local structured assessment is carried out on the remaining candidates,so as to realize accurate target tracking.Simulation results show that the proposed algorithm can significantly improve the accuracy and robustness of target tracking under severe illumination changes compared with the existing state-of-the-art algorithms.