Generalized ℓP-regularized representation for visual tracking

Collaborative representation has been successfully applied to visual tracking to powerfully use all the PCA basis vectors in the target subspace for object representation. However, collaborative representation always exists redundant features that may affect the performance of visual tracking. In this paper, a visual tracking algorithm is proposed by solving a generalized ℓp-regularized (0≤p≤1) problem within a Bayesian inference framework for the reduction of redundant features. To efficiently solve the minimization problem of ℓp-regularization, the Generalization of Soft-threshold (GST) operator is applied in the framework of iterative Accelerated Proximal Gradient (APG) approach. Moreover, the GST operator can also provide a unified framework to observe the effects of different sparsity for visual tracking. To show the feasibility of ℓp-regularizer, we choose the representative ℓ0.5-norm as the regularizer for the target coefficient and adjust the corresponding sparsity to be appropriate. Furthermore, we also introduce an extra ℓ0-regularized tracker to observe the effect of excessive sparsity in a unified framework. Experimental results on several challenging sequences demonstrate that the proposed tracker leads to a more favorable performance in terms of accuracy measures including the overlap ratio and center location error, respectively.