In order to improve the instantaneity of the tracking method based on sparse and lowrank matrix decomposition, a lowrank object tracking algorithm based on large matrix and compressed feature is proposed in this paper. The matrix is decomposed sparsely and lowrankly by creating observation matrix using segmenting the large matrix into some parts. Then the error vector of each candidate object is obtained and an error matrix is built. The tracking result is gained by resolving the least 1norm of the error matrix. To adapt to the changes of target appearance, the dictionary is selectively updated based on the discrimination of vector similarity. When the tracking result is not trusted, it is updated by trajectory rectification. The instantaneity of the new algorithm is three times the old one via the comparison results on six typical sequences. The experiments demonstrate that the proposed algorithm can track the object accurately and robustly when there is part occlusion, illumination change and fast motion.