Receding horizon rank minimization based estimation with applications to visual tracking

This paper addresses the problem of predicting future outputs of an unknown Linear Time Invariant System based solely on past input/output data corrupted by noise, and an a-priori bound on the system order. This situation arises in many scenarios of practical interest where an explicit a-priori model of the system is not available. The main result of the paper is a simple, computationally efficient tracking algorithm that does not entail identifying first the unknown dynamics. Rather, the problem of estimating the next value of the output is recast into a rank minimization problem and solved using some recently introduced convex relaxations. The potential of the proposed approach is illustrated using as an example the problem of tracking multiple targets in video sequences in the presence of occlusion.