Robust visual tracking via multi-view discriminant based sparse representation
Bin Kang, Dong Liang, Suofei Zhang · 2017
In traditional sparse representation based visual tracking, the particles are densely sampled, the appearance of some candidates may be very similar, hence the particle observations can be divided into disjointed groups. Existing methods only exploit the group similarity in a certain feature space. In this paper we propose a multi-view discriminant based multitask sparse representation method to exploit the group similarity in a multi-feature space. The proposed method can discriminate the reliability of observation groups and achieve a proper multi-view fusion by using a multi-view discriminant matrix to project multi-feature observation groups into a common subspace. Experiment results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.