Learning appearance manifolds with structured sparse representation for robust visual tracking

Abstract

This paper presents a novel algorithm for robust visual object tracking based on the structured sparse representation framework. Conventional structured sparse representation based tracker models the nonlinear appearance manifold with a single subspace that is difficult to handle significant pose and illumination changes. Different from the afore-mentioned method, the proposed algorithm approximates the nonlinear appearance manifold by multiple low dimensional subspaces computed by an incremental learning scheme based on the merging and insert strategy. 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 complex nonlinear appearance manifold can effectively represent by a sparse linear combination of structured union of subspaces. Experiments on benchmark video sequences show that the new structured sparse representation model improves the robustness of tracking.