Robust visual tracking algorithm based on bidirectional sparse representation

Abstract

At present the visual tracking model based on sparse representation is mainly divided into two types: one is to use the template set to reconstruct candidate samples, which is called forward model; the other is to project the template set into a candidate space, which is called reverse model. What the two models have in common is to compute the sparse correlation coefficient matrix of candidate sample and template set. Based on this, the paper establishes a bidirectional cooperative sparse representation tracking model. Using L2-norm constraint item, the forward and reverse sparse correlation matrix coefficients could be uniformly convergent. In comparison to conventional unidirectional sparse tracking model, bidirectional sparse tracking model could fully excavate the sparse mapping relation of the whole candidate sample and template set. And the candidate that scores highest in the sparse mapping table for the positive and negative templates is the tracking result. Based on the accelerated proximal gradient fast method, the paper derives the optimum solution (in matrix form) of bidirectional sparse tracking model. As a result, it allows the candidates and templates to be calculated in parallel, which can improve the calculation efficiency to some extent. Numerical examples show that the proposed tracking algorithm has certain priority over against the conventional unidirectional sparse tracking methods.