Robust Visual Tracking Using Flexible Structured Sparse Representation

Tianxiang Bai, Youfu Li

Abstract: 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 the occlusion as a sparse linear combination of structured union of subspaces in a basis library, which consists of multiple incremental learned target 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 flexible structured sparse representation based appearance model facilitates the tracking performance compared with the prototype structured sparse representation model and other state of the art tracking algorithms.