The invention discloses a local sparse representation object tracking method based on LO regularization. Different from traditional L1 object tracking methods, the method herein proposes the combination between a LO norm and a structural local sparse appearance model, fully utilizes sparse coding, better differentiates objects from backgrounds in the course of tracking, and models local shielding and the like interferences by using a trivial template, and further increases the robustness of noise interference in the course of tracking. In order to enable an object model to better deal with continuous changes of the appearance of an object in the course of tracking, according to the invention, the method, through the construction of an object model set, adopts the LO norm in reconstructing the object in the course of tracking and adopts probabilistic policy in replacing a certain template in the object template set with a reconstructing result, thus realizing the update of template dynamics and further increasing the stability of the algorithm. According to the invention, the method which is directed at the NP problem of the object function optimization solution based on the LO norm, employs the APG algorithm in realizing effective solution.