In order to improve the robustness of visual target tracking algorithm, a multiple feature fusion tracking algorithm is proposed based on covariance matrix. Under the framework of quantum genetic algorithm, the region covariance descriptor is used to fuse color, edge, and texture features. A fast covariance intersection algorithm is adopted to update the model. The proposed algorithm makes the most use of low dimension of the region covariance descriptor, fast convergence and strong global search ability of the quantum genetic algorithm, and fast calculation ability of the fast covariance intersection algorithm, which greatly improves the efficiency of fusing, matching and updating process, and effectively realizes fast and efficient multi-feature fusion tracking. Experimental results show that the proposed algorithm can effectively cope with the interference, such as occlusion, rotation, deformation and motion blur, and achieve fast and robust target tracking.