We propose an online tracking algorithm in which the object tracking is achieved by using subspace learning and non-negative matrix factorization (NMF) under the partile filtering framework. The object appearance is modeled by a non-negative combination of non-negative components learned from examples observed in previous frames. In order to robust tracking an object, group sparsity constraints are included to the non-negativity one. In addition, the Alternating Direction Method of Multipliers (ADMM) algorithm is proposed for efficient model updating. Qualitative and quantitative experiments on a variety of challenging sequences show favorable performance of the proposed algorithm against 9 state-of-the-art methods.