Object tracking based on the joint model using L2-norm minimization

The computational cost of the tracking algorithm based on the sparse representation is so much large,at the same time,the target apparence changes on account of a variety of reasons,which makes the object tracking process complicated and time consuming. A joint model is reasonably proposed by combining the global template based on the discriminant model and the generation model based on the local descriptor,properly solved by the L2-norm minimization solution in a bayesian inference framework,which is proved to be effective and efficient. In the process of the object tracking process,the plus template and the minus template of the discriminant model and the coefficient vector of the generative model are timely updated so as to have a strong adaptability and robust discrimination. The experimental results finally show that compared with other typical algorithms,the proposed algorithm has stronger robustness in the case of illumination,scale changes,shelter,rotation and so on.