Flexible structured sparse representation for robust visual tracking

In this work, we propose a robust and flexible appearance model based on the structured sparse representation framework. In our method, we model the complex nonlinear appearance manifold and occlusions as a sparse linear combination of structured union of subspaces in a basis library consisting of multiple learned low dimensional subspaces and a partitioned occlusion template set. In order to enhance the discriminative power of the model, a number of clustered background subspaces are also added into the basis library and updated during tracking. With the Block Orthogonal Matching Pursuit (BOMP) algorithm, we show that the new structured sparse representation based appearance model facilitates the tracking performance compared with the prototype model and other state of the art tracking algorithms.