Under the framework of the Bayesian inference

tracking methods based on sparse representations can deal with complex appearance changes in the video scene successfully and robustly.However

the computation costs are too expensive to achieve real-time tracking.To solve this problem

a new real-time tracking method based on L-norm minimization is proposed in this paper.The proposed method introduces the L norm minimization into the PCA reconstruction

removes trivial templates from the sparse tracking method and presents an effective object representation model based on the L-norm minimization.An observation likelihood function that takes occlusion into account is designed in this paper.The experiments on many challenging image sequences demonstrate that the proposed method achieves the same and even better results when compared with several state-of-the-art tracking algorithms. Furthermore

it runs fast with a speed of about 20 frames/s.The proposed method in this paper can handle occlusion

illumination changes

scale changes and no-rigid appearance changes effectively in video surveillance scenes with a lower computation complexity.Additionally

it can run in real-time.