In order to improve the performance of generative visual tracking unde r complex environment such as occlusion and background clutter,firstly, l₀ regularized constraint is introduced to sparse coding model to reduce the redun dant encoding information and improve the effect of objective apparent reconstruction.As a no ntrivial byproduct,a novel fast iterative algorithm based on the non-convex accelerated proximal gradient is proposed to solve the resulting non-convex and non-smooth optimization problems.Secondly,a incremental low-rank features le arning strategy is designed.Unlike the traditional methods which need to do low-rank learning on the whole objective o bservation data matrix,the strategy proposed in this paper can effectively learn and update objective low-rank features subs pace online by l₀ regularized sparse coding.Experimental results on multiple video sequences show that the method based on the l₀ regularized incremental low-rank features learning can effectively improve the accuracy and robustness of target tracking algorithm.Compared with the 8state-of-the-art target tracking algorithms,the proposed algorithm achieves the best results both in location error robustness and overlap rate robustness.