The spatio-temporal tracking (STC) algorithm can effectively track object using the structural information contained in the context around the object in real time. However the algorithm only exploits single gray object feature information in order to make the object representation discriminative. Moreover, it fails to initialize when tracking drift due to occlusion problems. Aiming at the existing weaknesses of the spatio-temporal context algorithm, a novel low-rank redetection based multiple feature fusion STC tracking algorithm is proposed in this paper. Firstly, multiple feature fusion based spatio-temporal context is extracted to construct complicated spatio-temporal context information, which improves the effectiveness of object representation by taking full advantage of the feature information around the object. Then, a simple and effective matrix decomposition method is used to give a low rank expression of the history tracking information, which can be embedded into the online detector. As a result, the uniform structure stability of the tracking algorithm is maintained to solve the relocation problem after the tracking failure. Experimental results on a series of tracking benchmark show the proposed algorithm has a better tracking precision and robustness than several stale-of-the-art methods, and it also have a good real-time performance.