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Object tracking algorithm has many challenges for stable and accurate performance, such as occlusion, noise, drifts and so on. Although many researches proposed many robust tracking algorithms to solve these problems, the solutions have not universality and real time. Additionally, in order to achieve robust tracking performance, a multiple model update strategy for visual tracking algorithm is proposed to adapt the target appearance changes. Moreover, for adapting complex appearance change, an update rate strategy has been proposed. The multiple models update strategy for visual tracking algorithm can process both infinitesimally small movements and abrupt changes simultaneously. Simulation experiments results have demonstrated that compared to its single model tracking algorithm, our proposed strategy can improves significantly the tracking stability and accuracy in benchmark datasets.