Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking

In traditional sparse representation based visual tracking, particle sampling is first achieved by particle filter method. Then the particle observations are represented by intensity feature. Finally, the visual tracking is achieved by the intensity feature based sparse representation model. Different from traditional sparse representation model, a canonical correlation analysis based sparse representation model is proposed in this paper. The proposed model first uses two kinds of features to represent the particle observations, then, the projections of particle observations are used to build the sparse representation model. The advantage of the proposed model lies in that it can give a proper multi-feature fusing through canonical correlation analysis, which explores the relation between two features in a latent common subspace.