Robust visual tracking using incremental sparse representation

Abstract

The sparse representation has achieved considerable success in visual tracking due to its simplicity and robustness. It requires each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an l1-regularized least squares problem. When the sparse representation is incorporated into the framework of particle filter, solving l1 minimization problem for each particle independently requires a large calculation time, making real-time implementation difficult. In this paper, we exploit the redundancy between particles and use the homotopy method to design an incremental likelihood function calculation approach, and therefore form an efficient and robust visual tracking algorithm. The proposed algorithm is tested on extensive video sequences and the experimental results are found to be highly competitive with other recent trackers. © 2013 Springer-Verlag.