Recovering low-rank and sparse components of matrices for object detection

It is shown that object detection can be addressed in the authors´ unified framework, where the observed video matrix is decomposed into the low-rank matrix and the sparse matrix. The recovering problem can be solved by the proposed variant of the Douglas-Rachford splitting method, which accomplishes recovery by exploiting the separable structure property of the model. The effectiveness of the proposed object detection scheme is illustrated on two data: simulated data and real sequences applications. The numerical experiments verify that the proposed algorithm has attractive robustness and high accuracy for illumination variation and dynamic texture.