Robust multi-feature visual tracking via multi-task kernel-based sparse learning

Feature selection and fusion is of crucial importance in multi-feature visual tracking. This study proposes a multi-task kernel-based sparse learning method for multi-feature visual tracking. The proposed sparse learning method can discriminate the reliable and unreliable features for optimal multi-feature fusion through using a Fisher discrimination criterion-based multi-objective model to adaptively train the kernel weights of different features such as pixel intensity, edge and texture. To guarantee a robustness of the sparse representation method, a mixed norm is employed in the sparse leaning method to adaptively select correlated particle observations for multi-task sparse reconstruction. Experimental results show that the proposed sparse learning method can achieve a better tracking performance than state-of-the-art tracking methods do.